From 1159ff83bb50d30c71529142dd89863a824d2c17 Mon Sep 17 00:00:00 2001
From: "d.a.bunin" <d.a.bunin@tinkoff.ru>
Date: Thu, 4 Aug 2022 12:16:24 +0300
Subject: [PATCH 01/22] Rename determine_num_steps_to_forecast to
 determine_num_steps

---
 etna/models/tbats.py            |  8 +++---
 etna/models/utils.py            | 49 ++++++++++++++++-----------------
 tests/test_models/test_utils.py | 42 ++++++++++++----------------
 3 files changed, 45 insertions(+), 54 deletions(-)

diff --git a/etna/models/tbats.py b/etna/models/tbats.py
index 42631245a..5809a65ab 100644
--- a/etna/models/tbats.py
+++ b/etna/models/tbats.py
@@ -11,7 +11,7 @@
 
 from etna.models.base import BaseAdapter
 from etna.models.base import PerSegmentPredictionIntervalModel
-from etna.models.utils import determine_num_steps_to_forecast
+from etna.models.utils import determine_num_steps
 
 
 class _TBATSAdapter(BaseAdapter):
@@ -43,8 +43,8 @@ def predict(self, df: pd.DataFrame, prediction_interval: bool, quantiles: Iterab
                 "In-sample predictions aren't supported by current implementation."
             )
 
-        steps_to_forecast = determine_num_steps_to_forecast(
-            last_train_timestamp=self._last_train_timestamp, last_test_timestamp=df["timestamp"].max(), freq=self._freq
+        steps_to_forecast = determine_num_steps(
+            start_timestamp=self._last_train_timestamp, end_timestamp=df["timestamp"].max(), freq=self._freq
         )
         steps_to_skip = steps_to_forecast - df.shape[0]
 
@@ -203,4 +203,4 @@ def __init__(
             multiprocessing_start_method=multiprocessing_start_method,
             context=context,
         )
-        super().__init__(base_model=_TBATSAdapter(self.model))
+        super().__init__(base_model=_TBATSAdapter(self.model))
\ No newline at end of file
diff --git a/etna/models/utils.py b/etna/models/utils.py
index 49cb5bb93..3bd67dca6 100644
--- a/etna/models/utils.py
+++ b/etna/models/utils.py
@@ -1,20 +1,15 @@
 import pandas as pd
 
 
-def determine_num_steps_to_forecast(
-    last_train_timestamp: pd.Timestamp, last_test_timestamp: pd.Timestamp, freq: str
-) -> int:
-    """Determine number of steps to make a forecast in future.
-
-    It is useful for out-sample forecast with gap if model predicts only on a certain number of steps
-    in autoregressive manner.
+def determine_num_steps(start_timestamp: pd.Timestamp, end_timestamp: pd.Timestamp, freq: str) -> int:
+    """Determine how many steps of ``freq`` should we make from ``start_timestamp`` to reach ``end_timestamp``.
 
     Parameters
     ----------
-    last_train_timestamp:
-        last timestamp in train data
-    last_test_timestamp:
-        last timestamp in test data, should be after ``last_train_timestamp``
+    start_timestamp:
+        timestamp to start counting from
+    end_timestamp:
+        timestamp to end counting, should be not earlier than ``start_timestamp``
     freq:
         pandas frequency string: `Offset aliases <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>`_
 
@@ -26,26 +21,30 @@ def determine_num_steps_to_forecast(
     Raises
     ------
     ValueError:
-        Value of last test timestamp is less or equal than last train timestamp
+        Value of end timestamp is less than start timestamp
     ValueError:
-        Last train timestamp isn't correct according to a given frequency
+        Start timestamp isn't correct according to a given frequency
     ValueError:
-        Last test timestamps isn't reachable with a given frequency
+        End timestamp isn't reachable with a given frequency
     """
-    if last_test_timestamp <= last_train_timestamp:
-        raise ValueError("Last train timestamp should be less than last test timestamp!")
+    if start_timestamp > end_timestamp:
+        raise ValueError("Start train timestamp should be less or equal than end timestamp!")
+
+    # check if start_timestamp is normalized
+    normalized_start_timestamp = pd.date_range(start=start_timestamp, periods=1, freq=freq)
+    if normalized_start_timestamp != start_timestamp:
+        raise ValueError(f"Start timestamp isn't correct according to given frequency: {freq}")
 
-    # check if last_train_timestamp is normalized
-    normalized_last_train_timestamp = pd.date_range(start=last_train_timestamp, periods=1, freq=freq)
-    if normalized_last_train_timestamp != last_train_timestamp:
-        raise ValueError(f"Last train timestamp isn't correct according to given frequency: {freq}")
+    # check a simple case
+    if start_timestamp == end_timestamp:
+        return 0
 
     # make linear probing, because for complex offsets there is a cycle in `pd.date_range`
     cur_value = 1
     while True:
-        timestamps = pd.date_range(start=last_train_timestamp, periods=cur_value + 1, freq=freq)
-        if timestamps[-1] == last_test_timestamp:
+        timestamps = pd.date_range(start=start_timestamp, periods=cur_value + 1, freq=freq)
+        if timestamps[-1] == end_timestamp:
             return cur_value
-        elif timestamps[-1] > last_test_timestamp:
-            raise ValueError(f"Last test timestamps isn't reachable with freq: {freq}")
-        cur_value += 1
+        elif timestamps[-1] > end_timestamp:
+            raise ValueError(f"End timestamp isn't reachable with freq: {freq}")
+        cur_value += 1
\ No newline at end of file
diff --git a/tests/test_models/test_utils.py b/tests/test_models/test_utils.py
index d269a5b99..3589598c4 100644
--- a/tests/test_models/test_utils.py
+++ b/tests/test_models/test_utils.py
@@ -1,64 +1,56 @@
 import pandas as pd
 import pytest
 
-from etna.models.utils import determine_num_steps_to_forecast
+from etna.models.utils import determine_num_steps
 
 
 @pytest.mark.parametrize(
-    "last_train_timestamp, last_test_timestamp, freq, answer",
+    "start_timestamp, end_timestamp, freq, answer",
     [
         (pd.Timestamp("2020-01-01"), pd.Timestamp("2020-01-02"), "D", 1),
         (pd.Timestamp("2020-01-01"), pd.Timestamp("2020-01-11"), "D", 10),
+        (pd.Timestamp("2020-01-01"), pd.Timestamp("2020-01-01"), "D", 0),
         (pd.Timestamp("2020-01-05"), pd.Timestamp("2020-01-19"), "W-SUN", 2),
         (pd.Timestamp("2020-01-01"), pd.Timestamp("2020-01-15"), pd.offsets.Week(), 2),
         (pd.Timestamp("2020-01-31"), pd.Timestamp("2021-02-28"), "M", 13),
         (pd.Timestamp("2020-01-01"), pd.Timestamp("2021-06-01"), "MS", 17),
     ],
 )
-def test_determine_num_steps_to_forecast_ok(last_train_timestamp, last_test_timestamp, freq, answer):
-    result = determine_num_steps_to_forecast(
-        last_train_timestamp=last_train_timestamp, last_test_timestamp=last_test_timestamp, freq=freq
-    )
+def test_determine_num_steps_ok(start_timestamp, end_timestamp, freq, answer):
+    result = determine_num_steps(start_timestamp=start_timestamp, end_timestamp=end_timestamp, freq=freq)
     assert result == answer
 
 
 @pytest.mark.parametrize(
-    "last_train_timestamp, last_test_timestamp, freq",
+    "start_timestamp, end_timestamp, freq",
     [
-        (pd.Timestamp("2020-01-01"), pd.Timestamp("2020-01-01"), "D"),
         (pd.Timestamp("2020-01-02"), pd.Timestamp("2020-01-01"), "D"),
     ],
 )
-def test_determine_num_steps_to_forecast_fail_wrong_order(last_train_timestamp, last_test_timestamp, freq):
-    with pytest.raises(ValueError, match="Last train timestamp should be less than last test timestamp"):
-        _ = determine_num_steps_to_forecast(
-            last_train_timestamp=last_train_timestamp, last_test_timestamp=last_test_timestamp, freq=freq
-        )
+def test_determine_num_steps_fail_wrong_order(start_timestamp, end_timestamp, freq):
+    with pytest.raises(ValueError, match="Start train timestamp should be less or equal than end timestamp"):
+        _ = determine_num_steps(start_timestamp=start_timestamp, end_timestamp=end_timestamp, freq=freq)
 
 
 @pytest.mark.parametrize(
-    "last_train_timestamp, last_test_timestamp, freq",
+    "start_timestamp, end_timestamp, freq",
     [
         (pd.Timestamp("2020-01-02"), pd.Timestamp("2020-06-01"), "M"),
         (pd.Timestamp("2020-01-02"), pd.Timestamp("2020-06-01"), "MS"),
     ],
 )
-def test_determine_num_steps_to_forecast_fail_wrong_start(last_train_timestamp, last_test_timestamp, freq):
-    with pytest.raises(ValueError, match="Last train timestamp isn't correct according to given frequency"):
-        _ = determine_num_steps_to_forecast(
-            last_train_timestamp=last_train_timestamp, last_test_timestamp=last_test_timestamp, freq=freq
-        )
+def test_determine_num_steps_fail_wrong_start(start_timestamp, end_timestamp, freq):
+    with pytest.raises(ValueError, match="Start timestamp isn't correct according to given frequency"):
+        _ = determine_num_steps(start_timestamp=start_timestamp, end_timestamp=end_timestamp, freq=freq)
 
 
 @pytest.mark.parametrize(
-    "last_train_timestamp, last_test_timestamp, freq",
+    "start_timestamp, end_timestamp, freq",
     [
         (pd.Timestamp("2020-01-31"), pd.Timestamp("2020-06-05"), "M"),
         (pd.Timestamp("2020-01-01"), pd.Timestamp("2020-06-05"), "MS"),
     ],
 )
-def test_determine_num_steps_to_forecast_fail_wrong_end(last_train_timestamp, last_test_timestamp, freq):
-    with pytest.raises(ValueError, match="Last test timestamps isn't reachable with freq"):
-        _ = determine_num_steps_to_forecast(
-            last_train_timestamp=last_train_timestamp, last_test_timestamp=last_test_timestamp, freq=freq
-        )
+def test_determine_num_steps_fail_wrong_end(start_timestamp, end_timestamp, freq):
+    with pytest.raises(ValueError, match="End timestamp isn't reachable with freq"):
+        _ = determine_num_steps(start_timestamp=start_timestamp, end_timestamp=end_timestamp, freq=freq)
\ No newline at end of file

From b719892c4492c8938bcc9cdf4d3811b874b0b389 Mon Sep 17 00:00:00 2001
From: "d.a.bunin" <d.a.bunin@tinkoff.ru>
Date: Thu, 4 Aug 2022 12:18:35 +0300
Subject: [PATCH 02/22] Rename determine_num_steps_to_forecast to
 determine_num_steps

---
 etna/models/tbats.py            | 2 +-
 etna/models/utils.py            | 2 +-
 tests/test_models/test_utils.py | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/etna/models/tbats.py b/etna/models/tbats.py
index 5809a65ab..629e63728 100644
--- a/etna/models/tbats.py
+++ b/etna/models/tbats.py
@@ -203,4 +203,4 @@ def __init__(
             multiprocessing_start_method=multiprocessing_start_method,
             context=context,
         )
-        super().__init__(base_model=_TBATSAdapter(self.model))
\ No newline at end of file
+        super().__init__(base_model=_TBATSAdapter(self.model))
diff --git a/etna/models/utils.py b/etna/models/utils.py
index 3bd67dca6..05dc0fde0 100644
--- a/etna/models/utils.py
+++ b/etna/models/utils.py
@@ -47,4 +47,4 @@ def determine_num_steps(start_timestamp: pd.Timestamp, end_timestamp: pd.Timesta
             return cur_value
         elif timestamps[-1] > end_timestamp:
             raise ValueError(f"End timestamp isn't reachable with freq: {freq}")
-        cur_value += 1
\ No newline at end of file
+        cur_value += 1
diff --git a/tests/test_models/test_utils.py b/tests/test_models/test_utils.py
index 3589598c4..2a4bf7153 100644
--- a/tests/test_models/test_utils.py
+++ b/tests/test_models/test_utils.py
@@ -53,4 +53,4 @@ def test_determine_num_steps_fail_wrong_start(start_timestamp, end_timestamp, fr
 )
 def test_determine_num_steps_fail_wrong_end(start_timestamp, end_timestamp, freq):
     with pytest.raises(ValueError, match="End timestamp isn't reachable with freq"):
-        _ = determine_num_steps(start_timestamp=start_timestamp, end_timestamp=end_timestamp, freq=freq)
\ No newline at end of file
+        _ = determine_num_steps(start_timestamp=start_timestamp, end_timestamp=end_timestamp, freq=freq)

From fb8443fb8fd90c2e3238916b39c519652630d975 Mon Sep 17 00:00:00 2001
From: "d.a.bunin" <d.a.bunin@tinkoff.ru>
Date: Thu, 4 Aug 2022 12:37:53 +0300
Subject: [PATCH 03/22] Fix SARIMAXModel to work with simple_differencing

---
 etna/models/sarimax.py | 46 +++++++++++++++++++++++++++++-------------
 pyproject.toml         |  2 +-
 2 files changed, 33 insertions(+), 15 deletions(-)

diff --git a/etna/models/sarimax.py b/etna/models/sarimax.py
index a26263317..ae473a778 100644
--- a/etna/models/sarimax.py
+++ b/etna/models/sarimax.py
@@ -6,11 +6,13 @@
 from typing import Tuple
 
 import pandas as pd
+from pmdarima.arima.arima import _seasonal_prediction_with_confidence
 from statsmodels.tools.sm_exceptions import ValueWarning
 from statsmodels.tsa.statespace.sarimax import SARIMAX
 
 from etna.models.base import BaseAdapter
 from etna.models.base import PerSegmentPredictionIntervalModel
+from etna.models.utils import determine_num_steps
 
 warnings.filterwarnings(
     message="No frequency information was provided, so inferred frequency .* will be used",
@@ -164,6 +166,8 @@ def __init__(
         self._model: Optional[SARIMAX] = None
         self._result: Optional[SARIMAX] = None
         self.regressor_columns: Optional[List[str]] = None
+        self._freq = None
+        self._first_train_timestamp = None
 
     def fit(self, df: pd.DataFrame, regressors: List[str]) -> "_SARIMAXAdapter":
         """
@@ -221,6 +225,14 @@ def fit(self, df: pd.DataFrame, regressors: List[str]) -> "_SARIMAXAdapter":
             **self.kwargs,
         )
         self._result = self._model.fit()
+
+        freq = pd.infer_freq(df["timestamp"], warn=False)
+        if freq is None:
+            raise ValueError("Can't determine frequency of a given dataframe")
+        self._freq = freq
+
+        self._first_train_timestamp = df["timestamp"].min()
+
         return self
 
     def predict(self, df: pd.DataFrame, prediction_interval: bool, quantiles: Sequence[float]) -> pd.DataFrame:
@@ -256,30 +268,36 @@ def predict(self, df: pd.DataFrame, prediction_interval: bool, quantiles: Sequen
             )
 
         exog_future = self._select_regressors(df)
+        start_timestamp = df["timestamp"].min()
+        end_timestamp = df["timestamp"].max()
+        start_idx = determine_num_steps(
+            start_timestamp=self._first_train_timestamp, end_timestamp=start_timestamp, freq=self._freq  # type: ignore
+        )
+        end_idx = determine_num_steps(
+            start_timestamp=self._first_train_timestamp, end_timestamp=end_timestamp, freq=self._freq  # type: ignore
+        )
         if prediction_interval:
-            forecast = self._result.get_prediction(
-                start=df["timestamp"].min(), end=df["timestamp"].max(), dynamic=False, exog=exog_future
+            forecast, _ = _seasonal_prediction_with_confidence(
+                arima_res=self._result, start=start_idx, end=end_idx, X=exog_future, alpha=0.05
             )
-            y_pred = forecast.predicted_mean
-            y_pred.name = "mean"
-            y_pred = pd.DataFrame(y_pred)
+            y_pred = pd.DataFrame({"mean": forecast})
             for quantile in quantiles:
                 # set alpha in the way to get a desirable quantile
                 alpha = min(quantile * 2, (1 - quantile) * 2)
-                borders = forecast.conf_int(alpha=alpha)
+                _, borders = _seasonal_prediction_with_confidence(
+                    arima_res=self._result, start=start_idx, end=end_idx, X=exog_future, alpha=alpha
+                )
                 if quantile < 1 / 2:
-                    series = borders["lower target"]
+                    series = borders[:, 0]
                 else:
-                    series = borders["upper target"]
+                    series = borders[:, 1]
                 y_pred[f"mean_{quantile:.4g}"] = series
         else:
-            forecast = self._result.get_prediction(
-                start=df["timestamp"].min(), end=df["timestamp"].max(), dynamic=False, exog=exog_future
+            forecast, _ = _seasonal_prediction_with_confidence(
+                arima_res=self._result, start=start_idx, end=end_idx, X=exog_future, alpha=0.05
             )
-            y_pred = forecast.predicted_mean
-            y_pred.name = "mean"
-            y_pred = pd.DataFrame(y_pred)
-        y_pred = y_pred.reset_index(drop=True, inplace=False)
+            y_pred = pd.DataFrame({"mean": forecast})
+
         rename_dict = {
             column: column.replace("mean", "target") for column in y_pred.columns if column.startswith("mean")
         }
diff --git a/pyproject.toml b/pyproject.toml
index abaf7987f..8977f5a98 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -49,7 +49,7 @@ ruptures = "1.1.5"
 numba = ">=0.53.1,<0.56.0"
 seaborn = "^0.11.1"
 statsmodels = ">=0.12,<0.14"
-pmdarima = ">=1.8.0"
+pmdarima = ">=1.8.3"
 dill = "^0.3.4"
 toml = "^0.10.2"
 loguru = "^0.5.3"

From 2a1fc0904753b34e5c715d9610aa7c53ed00e72a Mon Sep 17 00:00:00 2001
From: "d.a.bunin" <d.a.bunin@tinkoff.ru>
Date: Thu, 4 Aug 2022 13:26:16 +0300
Subject: [PATCH 04/22] Update lock, add tests on simple_differencing

---
 etna/models/sarimax.py                  |    4 +-
 poetry.lock                             | 1630 ++---------------------
 tests/test_models/test_sarimax_model.py |   26 +
 3 files changed, 172 insertions(+), 1488 deletions(-)

diff --git a/etna/models/sarimax.py b/etna/models/sarimax.py
index ae473a778..b7f4b950d 100644
--- a/etna/models/sarimax.py
+++ b/etna/models/sarimax.py
@@ -197,8 +197,8 @@ def fit(self, df: pd.DataFrame, regressors: List[str]) -> "_SARIMAXAdapter":
 
         self._check_df(df)
 
-        targets = df["target"]
-        targets.index = df["timestamp"]
+        # make it a numpy array for forgetting about indices
+        targets = df["target"].values
 
         exog_train = self._select_regressors(df)
 
diff --git a/poetry.lock b/poetry.lock
index 7262fa919..67dc73c47 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -413,9 +413,9 @@ tqdm = "*"
 ujson = "*"
 
 [package.extras]
+tests = ["types-ujson", "xarray", "tqdm", "testfixtures", "mypy", "pytest-order", "pytest-cov", "pytest", "pylint", "flake8"]
+docs = ["matplotlib", "numpydoc", "sphinx-rtd-theme", "sphinx-gallery", "sphinx"]
 all = ["xarray"]
-docs = ["sphinx", "sphinx-gallery", "sphinx-rtd-theme", "numpydoc", "matplotlib"]
-tests = ["flake8", "pylint", "pytest", "pytest-cov", "pytest-order", "mypy", "testfixtures", "tqdm", "xarray", "types-ujson"]
 
 [[package]]
 name = "codespell"
@@ -463,9 +463,9 @@ python-versions = "<4,>=3.7"
 pymeeus = ">=0.3.13,<=1"
 
 [package.extras]
-dev = ["build", "black", "isort", "pylint"]
-docs = ["sphinx", "sphinx-rtd-theme", "myst-parser"]
 tests = ["coverage"]
+docs = ["myst-parser", "sphinx-rtd-theme", "sphinx"]
+dev = ["pylint", "isort", "black", "build"]
 
 [[package]]
 name = "coverage"
@@ -685,7 +685,7 @@ python-versions = ">=3.7"
 
 [[package]]
 name = "fsspec"
-version = "2022.5.0"
+version = "2022.7.1"
 description = "File-system specification"
 category = "main"
 optional = true
@@ -1534,7 +1534,7 @@ Jinja2 = ">=2.10"
 sphinx = ">=3.0"
 
 [package.extras]
-testing = ["pytest", "pytest-cov", "matplotlib"]
+testing = ["matplotlib", "pytest-cov", "pytest"]
 
 [[package]]
 name = "oauthlib"
@@ -2187,7 +2187,7 @@ python-versions = ">=3.7"
 packaging = "*"
 
 [package.extras]
-test = ["pytest (>=6,!=7.0.0,!=7.0.1)", "pytest-cov (>=3.0.0)", "pytest-qt"]
+test = ["pytest-qt", "pytest-cov (>=3.0.0)", "pytest (>=6,!=7.0.0,!=7.0.1)"]
 
 [[package]]
 name = "requests"
@@ -2280,10 +2280,10 @@ scipy = ">=1.1.0"
 threadpoolctl = ">=2.0.0"
 
 [package.extras]
-benchmark = ["matplotlib (>=2.2.3)", "pandas (>=0.25.0)", "memory-profiler (>=0.57.0)"]
-docs = ["matplotlib (>=2.2.3)", "scikit-image (>=0.14.5)", "pandas (>=0.25.0)", "seaborn (>=0.9.0)", "memory-profiler (>=0.57.0)", "sphinx (>=4.0.1)", "sphinx-gallery (>=0.7.0)", "numpydoc (>=1.0.0)", "Pillow (>=7.1.2)", "sphinx-prompt (>=1.3.0)", "sphinxext-opengraph (>=0.4.2)"]
-examples = ["matplotlib (>=2.2.3)", "scikit-image (>=0.14.5)", "pandas (>=0.25.0)", "seaborn (>=0.9.0)"]
-tests = ["matplotlib (>=2.2.3)", "scikit-image (>=0.14.5)", "pandas (>=0.25.0)", "pytest (>=5.0.1)", "pytest-cov (>=2.9.0)", "flake8 (>=3.8.2)", "black (>=21.6b0)", "mypy (>=0.770)", "pyamg (>=4.0.0)"]
+tests = ["pyamg (>=4.0.0)", "mypy (>=0.770)", "black (>=21.6b0)", "flake8 (>=3.8.2)", "pytest-cov (>=2.9.0)", "pytest (>=5.0.1)", "pandas (>=0.25.0)", "scikit-image (>=0.14.5)", "matplotlib (>=2.2.3)"]
+examples = ["seaborn (>=0.9.0)", "pandas (>=0.25.0)", "scikit-image (>=0.14.5)", "matplotlib (>=2.2.3)"]
+docs = ["sphinxext-opengraph (>=0.4.2)", "sphinx-prompt (>=1.3.0)", "Pillow (>=7.1.2)", "numpydoc (>=1.0.0)", "sphinx-gallery (>=0.7.0)", "sphinx (>=4.0.1)", "memory-profiler (>=0.57.0)", "seaborn (>=0.9.0)", "pandas (>=0.25.0)", "scikit-image (>=0.14.5)", "matplotlib (>=2.2.3)"]
+benchmark = ["memory-profiler (>=0.57.0)", "pandas (>=0.25.0)", "matplotlib (>=2.2.3)"]
 
 [[package]]
 name = "scipy"
@@ -2647,7 +2647,7 @@ scikit-learn = "*"
 scipy = "*"
 
 [package.extras]
-dev = ["pip-tools", "rpy2"]
+dev = ["rpy2", "pip-tools"]
 
 [[package]]
 name = "tenacity"
@@ -2733,8 +2733,8 @@ python-versions = ">=3.6"
 webencodings = ">=0.4"
 
 [package.extras]
-doc = ["sphinx", "sphinx-rtd-theme"]
-test = ["pytest", "pytest-cov", "pytest-flake8", "pytest-isort", "coverage"]
+test = ["coverage", "pytest-isort", "pytest-flake8", "pytest-cov", "pytest"]
+doc = ["sphinx-rtd-theme", "sphinx"]
 
 [[package]]
 name = "tokenize-rt"
@@ -2786,14 +2786,14 @@ torch = ">=1.3.1"
 typing-extensions = {version = "*", markers = "python_version < \"3.8\""}
 
 [package.extras]
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-    {file = "ujson-5.4.0-pp38-pypy38_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ef985eb2770900a485431910bd3f333b56d1a34b65f8c26a6ed8e8adf55f98d9"},
-    {file = "ujson-5.4.0-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:025758cf6561af6986d77cd4af9367ab56dde5c7c50f13f59e6964b4b25df73e"},
-    {file = "ujson-5.4.0.tar.gz", hash = "sha256:6b953e09441e307504130755e5bd6b15850178d591f66292bba4608c4f7f9b00"},
-]
+typing-extensions = []
+ujson = []
 urllib3 = []
 wandb = []
 wcwidth = [
@@ -5257,18 +3924,9 @@ webencodings = [
     {file = "webencodings-0.5.1-py2.py3-none-any.whl", hash = "sha256:a0af1213f3c2226497a97e2b3aa01a7e4bee4f403f95be16fc9acd2947514a78"},
     {file = "webencodings-0.5.1.tar.gz", hash = "sha256:b36a1c245f2d304965eb4e0a82848379241dc04b865afcc4aab16748587e1923"},
 ]
-werkzeug = [
-    {file = "Werkzeug-2.1.2-py3-none-any.whl", hash = "sha256:72a4b735692dd3135217911cbeaa1be5fa3f62bffb8745c5215420a03dc55255"},
-    {file = "Werkzeug-2.1.2.tar.gz", hash = "sha256:1ce08e8093ed67d638d63879fd1ba3735817f7a80de3674d293f5984f25fb6e6"},
-]
-widgetsnbextension = [
-    {file = "widgetsnbextension-3.6.1-py2.py3-none-any.whl", hash = "sha256:954e0faefdd414e4e013f17dbc7fd86f24cf1d243a3ac85d5f0fc2c2d2b50c66"},
-    {file = "widgetsnbextension-3.6.1.tar.gz", hash = "sha256:9c84ae64c2893c7cbe2eaafc7505221a795c27d68938454034ac487319a75b10"},
-]
-win32-setctime = [
-    {file = "win32_setctime-1.1.0-py3-none-any.whl", hash = "sha256:231db239e959c2fe7eb1d7dc129f11172354f98361c4fa2d6d2d7e278baa8aad"},
-    {file = "win32_setctime-1.1.0.tar.gz", hash = "sha256:15cf5750465118d6929ae4de4eb46e8edae9a5634350c01ba582df868e932cb2"},
-]
+werkzeug = []
+widgetsnbextension = []
+win32-setctime = []
 wrapt = []
 yarl = [
     {file = "yarl-1.7.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:f2a8508f7350512434e41065684076f640ecce176d262a7d54f0da41d99c5a95"},
diff --git a/tests/test_models/test_sarimax_model.py b/tests/test_models/test_sarimax_model.py
index 3a7d6c299..7732a58a5 100644
--- a/tests/test_models/test_sarimax_model.py
+++ b/tests/test_models/test_sarimax_model.py
@@ -1,6 +1,9 @@
+import numpy as np
 import pytest
 from statsmodels.tsa.statespace.sarimax import SARIMAX
 
+from etna.datasets import TSDataset
+from etna.datasets import generate_ar_df
 from etna.models import SARIMAXModel
 from etna.pipeline import Pipeline
 
@@ -134,3 +137,26 @@ def test_sarimax_forecast_1_point(example_tsds):
     assert len(pred.df) == horizon
     pred_quantiles = model.forecast(future_ts, prediction_interval=True, quantiles=[0.025, 0.8])
     assert len(pred_quantiles.df) == horizon
+
+
+def test_prediction_simple_differencing():
+    horizon = 7
+    df = generate_ar_df(periods=100, n_segments=3, start_time="2020-01-01")
+    ts = TSDataset(df=TSDataset.to_dataset(df), freq="D")
+
+    # prepare prediction from regular model
+    model_regular = SARIMAXModel(order=(1, 1, 1))
+    model_regular.fit(ts)
+    future_ts = ts.make_future(future_steps=horizon)
+    regular_prediction = model_regular.forecast(future_ts)
+    regular_prediction = regular_prediction.to_pandas(flatten=True)
+
+    # prepare prediction from model with simple differencing
+    model_simplified = SARIMAXModel(order=(1, 1, 1), simple_differencing=True)
+    model_simplified.fit(ts)
+    future_ts = ts.make_future(future_steps=horizon)
+    simplified_prediction = model_simplified.forecast(future_ts)
+    simplified_prediction = simplified_prediction.to_pandas(flatten=True)
+
+    correlation = np.corrcoef(regular_prediction["target"], simplified_prediction["target"])[0, 1]
+    assert correlation >= 0.95

From ea49210d7e1c211d63b8c8a9e44596cacb55d625 Mon Sep 17 00:00:00 2001
From: "d.a.bunin" <d.a.bunin@tinkoff.ru>
Date: Thu, 4 Aug 2022 13:30:15 +0300
Subject: [PATCH 05/22] Get lock back to master:

---
 poetry.lock | 1630 ++++++++++++++++++++++++++++++++++++++++++++++-----
 1 file changed, 1486 insertions(+), 144 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 67dc73c47..7262fa919 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -413,9 +413,9 @@ tqdm = "*"
 ujson = "*"
 
 [package.extras]
-tests = ["types-ujson", "xarray", "tqdm", "testfixtures", "mypy", "pytest-order", "pytest-cov", "pytest", "pylint", "flake8"]
-docs = ["matplotlib", "numpydoc", "sphinx-rtd-theme", "sphinx-gallery", "sphinx"]
 all = ["xarray"]
+docs = ["sphinx", "sphinx-gallery", "sphinx-rtd-theme", "numpydoc", "matplotlib"]
+tests = ["flake8", "pylint", "pytest", "pytest-cov", "pytest-order", "mypy", "testfixtures", "tqdm", "xarray", "types-ujson"]
 
 [[package]]
 name = "codespell"
@@ -463,9 +463,9 @@ python-versions = "<4,>=3.7"
 pymeeus = ">=0.3.13,<=1"
 
 [package.extras]
+dev = ["build", "black", "isort", "pylint"]
+docs = ["sphinx", "sphinx-rtd-theme", "myst-parser"]
 tests = ["coverage"]
-docs = ["myst-parser", "sphinx-rtd-theme", "sphinx"]
-dev = ["pylint", "isort", "black", "build"]
 
 [[package]]
 name = "coverage"
@@ -685,7 +685,7 @@ python-versions = ">=3.7"
 
 [[package]]
 name = "fsspec"
-version = "2022.7.1"
+version = "2022.5.0"
 description = "File-system specification"
 category = "main"
 optional = true
@@ -1534,7 +1534,7 @@ Jinja2 = ">=2.10"
 sphinx = ">=3.0"
 
 [package.extras]
-testing = ["matplotlib", "pytest-cov", "pytest"]
+testing = ["pytest", "pytest-cov", "matplotlib"]
 
 [[package]]
 name = "oauthlib"
@@ -2187,7 +2187,7 @@ python-versions = ">=3.7"
 packaging = "*"
 
 [package.extras]
-test = ["pytest-qt", "pytest-cov (>=3.0.0)", "pytest (>=6,!=7.0.0,!=7.0.1)"]
+test = ["pytest (>=6,!=7.0.0,!=7.0.1)", "pytest-cov (>=3.0.0)", "pytest-qt"]
 
 [[package]]
 name = "requests"
@@ -2280,10 +2280,10 @@ scipy = ">=1.1.0"
 threadpoolctl = ">=2.0.0"
 
 [package.extras]
-tests = ["pyamg (>=4.0.0)", "mypy (>=0.770)", "black (>=21.6b0)", "flake8 (>=3.8.2)", "pytest-cov (>=2.9.0)", "pytest (>=5.0.1)", "pandas (>=0.25.0)", "scikit-image (>=0.14.5)", "matplotlib (>=2.2.3)"]
-examples = ["seaborn (>=0.9.0)", "pandas (>=0.25.0)", "scikit-image (>=0.14.5)", "matplotlib (>=2.2.3)"]
-docs = ["sphinxext-opengraph (>=0.4.2)", "sphinx-prompt (>=1.3.0)", "Pillow (>=7.1.2)", "numpydoc (>=1.0.0)", "sphinx-gallery (>=0.7.0)", "sphinx (>=4.0.1)", "memory-profiler (>=0.57.0)", "seaborn (>=0.9.0)", "pandas (>=0.25.0)", "scikit-image (>=0.14.5)", "matplotlib (>=2.2.3)"]
-benchmark = ["memory-profiler (>=0.57.0)", "pandas (>=0.25.0)", "matplotlib (>=2.2.3)"]
+benchmark = ["matplotlib (>=2.2.3)", "pandas (>=0.25.0)", "memory-profiler (>=0.57.0)"]
+docs = ["matplotlib (>=2.2.3)", "scikit-image (>=0.14.5)", "pandas (>=0.25.0)", "seaborn (>=0.9.0)", "memory-profiler (>=0.57.0)", "sphinx (>=4.0.1)", "sphinx-gallery (>=0.7.0)", "numpydoc (>=1.0.0)", "Pillow (>=7.1.2)", "sphinx-prompt (>=1.3.0)", "sphinxext-opengraph (>=0.4.2)"]
+examples = ["matplotlib (>=2.2.3)", "scikit-image (>=0.14.5)", "pandas (>=0.25.0)", "seaborn (>=0.9.0)"]
+tests = ["matplotlib (>=2.2.3)", "scikit-image (>=0.14.5)", "pandas (>=0.25.0)", "pytest (>=5.0.1)", "pytest-cov (>=2.9.0)", "flake8 (>=3.8.2)", "black (>=21.6b0)", "mypy (>=0.770)", "pyamg (>=4.0.0)"]
 
 [[package]]
 name = "scipy"
@@ -2647,7 +2647,7 @@ scikit-learn = "*"
 scipy = "*"
 
 [package.extras]
-dev = ["rpy2", "pip-tools"]
+dev = ["pip-tools", "rpy2"]
 
 [[package]]
 name = "tenacity"
@@ -2733,8 +2733,8 @@ python-versions = ">=3.6"
 webencodings = ">=0.4"
 
 [package.extras]
-test = ["coverage", "pytest-isort", "pytest-flake8", "pytest-cov", "pytest"]
-doc = ["sphinx-rtd-theme", "sphinx"]
+doc = ["sphinx", "sphinx-rtd-theme"]
+test = ["pytest", "pytest-cov", "pytest-flake8", "pytest-isort", "coverage"]
 
 [[package]]
 name = "tokenize-rt"
@@ -2786,14 +2786,14 @@ torch = ">=1.3.1"
 typing-extensions = {version = "*", markers = "python_version < \"3.8\""}
 
 [package.extras]
-text = ["tqdm (>=4.41.0)", "regex (>=2021.9.24)", "nltk (>=3.6)"]
-test = ["fire", "mir-eval (>=0.6)", "check-manifest", "phmdoctest (>=1.1.1)", "pytest-timeout", "pytest-doctestplus (>=0.9.0)", "scikit-learn (>1.0,<1.1.1)", "twine (>=3.2)", "pytest (>=6.0.0,<7.0.0)", "requests", "pre-commit (>=1.0)", "psutil", "pytest-cov (>2.10)", "sacrebleu (>=2.0.0)", "huggingface-hub (<0.7)", "codecov (>=2.1)", "coverage (>5.2)", "pypesq", "jiwer (>=2.3.0)", "cloudpickle (>=1.3)", "bert-score (==0.3.10)", "torch-complex", "rouge-score (>=0.0.4)", "pycocotools", "fast-bss-eval (>=0.1.0)", "scikit-image (>0.17.1)", "pytorch-msssim", "transformers (>=4.0)", "mypy (>=0.790)"]
-integrate = ["pytorch-lightning (>=1.5)"]
-image = ["scipy", "lpips", "torch-fidelity", "torchvision"]
-docs = ["sphinxcontrib-mockautodoc", "pandoc (>=1.0)", "sphinx-togglebutton (>=0.2)", "myst-parser", "sphinx (>=4.0,<5.0)", "sphinx-autodoc-typehints (>=1.0)", "sphinx-paramlinks (>=0.5.1)", "sphinxcontrib-fulltoc (>=1.0)", "docutils (>=0.16)", "nbsphinx (>=0.8)", "sphinx-copybutton (>=0.3)"]
-detection = ["pycocotools", "torchvision (>=0.8)"]
+all = ["pystoi", "torchvision (>=0.8)", "pycocotools", "sphinx-copybutton (>=0.3)", "nbsphinx (>=0.8)", "docutils (>=0.16)", "sphinxcontrib-fulltoc (>=1.0)", "sphinx-paramlinks (>=0.5.1)", "sphinx-autodoc-typehints (>=1.0)", "sphinx (>=4.0,<5.0)", "myst-parser", "sphinx-togglebutton (>=0.2)", "pandoc (>=1.0)", "sphinxcontrib-mockautodoc", "torchvision", "torch-fidelity", "lpips", "scipy", "pytorch-lightning (>=1.5)", "mypy (>=0.790)", "transformers (>=4.0)", "pytorch-msssim", "scikit-image (>0.17.1)", "fast-bss-eval (>=0.1.0)", "rouge-score (>=0.0.4)", "torch-complex", "bert-score (==0.3.10)", "cloudpickle (>=1.3)", "jiwer (>=2.3.0)", "pypesq", "coverage (>5.2)", "codecov (>=2.1)", "huggingface-hub (<0.7)", "sacrebleu (>=2.0.0)", "pytest-cov (>2.10)", "psutil", "pre-commit (>=1.0)", "requests", "pytest (>=6.0.0,<7.0.0)", "twine (>=3.2)", "scikit-learn (>1.0,<1.1.1)", "pytest-doctestplus (>=0.9.0)", "pytest-timeout", "phmdoctest (>=1.1.1)", "check-manifest", "mir-eval (>=0.6)", "fire", "nltk (>=3.6)", "regex (>=2021.9.24)", "tqdm (>=4.41.0)"]
 audio = ["pystoi"]
-all = ["tqdm (>=4.41.0)", "regex (>=2021.9.24)", "nltk (>=3.6)", "fire", "mir-eval (>=0.6)", "check-manifest", "phmdoctest (>=1.1.1)", "pytest-timeout", "pytest-doctestplus (>=0.9.0)", "scikit-learn (>1.0,<1.1.1)", "twine (>=3.2)", "pytest (>=6.0.0,<7.0.0)", "requests", "pre-commit (>=1.0)", "psutil", "pytest-cov (>2.10)", "sacrebleu (>=2.0.0)", "huggingface-hub (<0.7)", "codecov (>=2.1)", "coverage (>5.2)", "pypesq", "jiwer (>=2.3.0)", "cloudpickle (>=1.3)", "bert-score (==0.3.10)", "torch-complex", "rouge-score (>=0.0.4)", "fast-bss-eval (>=0.1.0)", "scikit-image (>0.17.1)", "pytorch-msssim", "transformers (>=4.0)", "mypy (>=0.790)", "pytorch-lightning (>=1.5)", "scipy", "lpips", "torch-fidelity", "torchvision", "sphinxcontrib-mockautodoc", "pandoc (>=1.0)", "sphinx-togglebutton (>=0.2)", "myst-parser", "sphinx (>=4.0,<5.0)", "sphinx-autodoc-typehints (>=1.0)", "sphinx-paramlinks (>=0.5.1)", "sphinxcontrib-fulltoc (>=1.0)", "docutils (>=0.16)", "nbsphinx (>=0.8)", "sphinx-copybutton (>=0.3)", "pycocotools", "torchvision (>=0.8)", "pystoi"]
+detection = ["torchvision (>=0.8)", "pycocotools"]
+docs = ["sphinx-copybutton (>=0.3)", "nbsphinx (>=0.8)", "docutils (>=0.16)", "sphinxcontrib-fulltoc (>=1.0)", "sphinx-paramlinks (>=0.5.1)", "sphinx-autodoc-typehints (>=1.0)", "sphinx (>=4.0,<5.0)", "myst-parser", "sphinx-togglebutton (>=0.2)", "pandoc (>=1.0)", "sphinxcontrib-mockautodoc"]
+image = ["torchvision", "torch-fidelity", "lpips", "scipy"]
+integrate = ["pytorch-lightning (>=1.5)"]
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+    {file = "ujson-5.4.0-pp37-pypy37_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:67f4e2fa81e1d99c01e7b1978ab0cbf3c9a8b663f683a709f87baad110d5b940"},
+    {file = "ujson-5.4.0-pp37-pypy37_pp73-win_amd64.whl", hash = "sha256:9ae1d0094ce730e39e09656bc14074d9573cdd80adec1a55b06d8bf1f9613a01"},
+    {file = "ujson-5.4.0-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:784dbd12925845a3f0757a956447e2fd31418abb5aeaebf3aca1203195f16fd1"},
+    {file = "ujson-5.4.0-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:422653083c6df6cec17fdb5d6106c209aad9b0c94131c53b073980403db22167"},
+    {file = "ujson-5.4.0-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e91947fda8354ea7faf698b084ebcdbabd239e7b15d8436fb74394f59a207ac9"},
+    {file = "ujson-5.4.0-pp38-pypy38_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ef985eb2770900a485431910bd3f333b56d1a34b65f8c26a6ed8e8adf55f98d9"},
+    {file = "ujson-5.4.0-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:025758cf6561af6986d77cd4af9367ab56dde5c7c50f13f59e6964b4b25df73e"},
+    {file = "ujson-5.4.0.tar.gz", hash = "sha256:6b953e09441e307504130755e5bd6b15850178d591f66292bba4608c4f7f9b00"},
+]
 urllib3 = []
 wandb = []
 wcwidth = [
@@ -3924,9 +5257,18 @@ webencodings = [
     {file = "webencodings-0.5.1-py2.py3-none-any.whl", hash = "sha256:a0af1213f3c2226497a97e2b3aa01a7e4bee4f403f95be16fc9acd2947514a78"},
     {file = "webencodings-0.5.1.tar.gz", hash = "sha256:b36a1c245f2d304965eb4e0a82848379241dc04b865afcc4aab16748587e1923"},
 ]
-werkzeug = []
-widgetsnbextension = []
-win32-setctime = []
+werkzeug = [
+    {file = "Werkzeug-2.1.2-py3-none-any.whl", hash = "sha256:72a4b735692dd3135217911cbeaa1be5fa3f62bffb8745c5215420a03dc55255"},
+    {file = "Werkzeug-2.1.2.tar.gz", hash = "sha256:1ce08e8093ed67d638d63879fd1ba3735817f7a80de3674d293f5984f25fb6e6"},
+]
+widgetsnbextension = [
+    {file = "widgetsnbextension-3.6.1-py2.py3-none-any.whl", hash = "sha256:954e0faefdd414e4e013f17dbc7fd86f24cf1d243a3ac85d5f0fc2c2d2b50c66"},
+    {file = "widgetsnbextension-3.6.1.tar.gz", hash = "sha256:9c84ae64c2893c7cbe2eaafc7505221a795c27d68938454034ac487319a75b10"},
+]
+win32-setctime = [
+    {file = "win32_setctime-1.1.0-py3-none-any.whl", hash = "sha256:231db239e959c2fe7eb1d7dc129f11172354f98361c4fa2d6d2d7e278baa8aad"},
+    {file = "win32_setctime-1.1.0.tar.gz", hash = "sha256:15cf5750465118d6929ae4de4eb46e8edae9a5634350c01ba582df868e932cb2"},
+]
 wrapt = []
 yarl = [
     {file = "yarl-1.7.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:f2a8508f7350512434e41065684076f640ecce176d262a7d54f0da41d99c5a95"},

From f411a3440b616ce19b1362e118bfcfa24a80b54d Mon Sep 17 00:00:00 2001
From: "d.a.bunin" <d.a.bunin@tinkoff.ru>
Date: Thu, 4 Aug 2022 13:31:51 +0300
Subject: [PATCH 06/22] Update changelog

---
 CHANGELOG.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 53efb2488..f8d18351e 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -37,7 +37,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - 
 - 
 - 
-- 
+- Fix behavior of SARIMAXModel if simple_differencing=True is set ([#837](https://github.com/tinkoff-ai/etna/pull/837))
 - 
 - 
 - 

From 75925b1c10c2e56d6b750fc67c8e60567f34c29f Mon Sep 17 00:00:00 2001
From: "d.a.bunin" <d.a.bunin@tinkoff.ru>
Date: Thu, 4 Aug 2022 13:33:10 +0300
Subject: [PATCH 07/22] Add more precise comment

---
 etna/models/sarimax.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/etna/models/sarimax.py b/etna/models/sarimax.py
index b7f4b950d..d600584e4 100644
--- a/etna/models/sarimax.py
+++ b/etna/models/sarimax.py
@@ -197,7 +197,7 @@ def fit(self, df: pd.DataFrame, regressors: List[str]) -> "_SARIMAXAdapter":
 
         self._check_df(df)
 
-        # make it a numpy array for forgetting about indices
+        # make it a numpy array for forgetting about indices, it is necessary for _seasonal_prediction_with_confidence
         targets = df["target"].values
 
         exog_train = self._select_regressors(df)

From 9d74d7f361c1b9276e4951a66977c14e9d89f538 Mon Sep 17 00:00:00 2001
From: "d.a.bunin" <d.a.bunin@tinkoff.ru>
Date: Thu, 4 Aug 2022 16:21:43 +0300
Subject: [PATCH 08/22] Clarify test case for simple_differencing

---
 tests/test_models/test_sarimax_model.py | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/tests/test_models/test_sarimax_model.py b/tests/test_models/test_sarimax_model.py
index 7732a58a5..f5a34a7c3 100644
--- a/tests/test_models/test_sarimax_model.py
+++ b/tests/test_models/test_sarimax_model.py
@@ -140,19 +140,23 @@ def test_sarimax_forecast_1_point(example_tsds):
 
 
 def test_prediction_simple_differencing():
+    """Check that SARIMAX gives similar results with different values of ``simple_differencing``.
+
+    We generate dataset from ``generate_ar_df`` with ``ar_coef=[1]`` and it gives us (0, 1, 1) process.
+    """
     horizon = 7
-    df = generate_ar_df(periods=100, n_segments=3, start_time="2020-01-01")
+    df = generate_ar_df(periods=100, n_segments=3, start_time="2020-01-01", ar_coef=[1])
     ts = TSDataset(df=TSDataset.to_dataset(df), freq="D")
 
     # prepare prediction from regular model
-    model_regular = SARIMAXModel(order=(1, 1, 1))
+    model_regular = SARIMAXModel(order=(0, 1, 1))
     model_regular.fit(ts)
     future_ts = ts.make_future(future_steps=horizon)
     regular_prediction = model_regular.forecast(future_ts)
     regular_prediction = regular_prediction.to_pandas(flatten=True)
 
     # prepare prediction from model with simple differencing
-    model_simplified = SARIMAXModel(order=(1, 1, 1), simple_differencing=True)
+    model_simplified = SARIMAXModel(order=(0, 1, 1), simple_differencing=True)
     model_simplified.fit(ts)
     future_ts = ts.make_future(future_steps=horizon)
     simplified_prediction = model_simplified.forecast(future_ts)

From 47f75d4ca0664f1ae13ec997c9dfb6282529d6d2 Mon Sep 17 00:00:00 2001
From: "d.a.bunin" <d.a.bunin@tinkoff.ru>
Date: Thu, 4 Aug 2022 16:53:31 +0300
Subject: [PATCH 09/22] Add seasonal_prediction_with_confidence as external
 code

---
 etna/libs/pmdarima/__init__.py |  1 +
 etna/libs/pmdarima/arima.py    | 92 ++++++++++++++++++++++++++++++++++
 etna/models/sarimax.py         |  8 +--
 3 files changed, 97 insertions(+), 4 deletions(-)
 create mode 100644 etna/libs/pmdarima/__init__.py
 create mode 100644 etna/libs/pmdarima/arima.py

diff --git a/etna/libs/pmdarima/__init__.py b/etna/libs/pmdarima/__init__.py
new file mode 100644
index 000000000..a04d2fad6
--- /dev/null
+++ b/etna/libs/pmdarima/__init__.py
@@ -0,0 +1 @@
+from arima import seasonal_prediction_with_confidence
diff --git a/etna/libs/pmdarima/arima.py b/etna/libs/pmdarima/arima.py
new file mode 100644
index 000000000..371e902f3
--- /dev/null
+++ b/etna/libs/pmdarima/arima.py
@@ -0,0 +1,92 @@
+"""
+MIT License
+
+Copyright (c) 2017 Taylor G Smith
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+"""
+
+import numpy as np
+import numpy.polynomial.polynomial as np_polynomial
+from sklearn.utils.validation import check_array
+from pmdarima.arima import ARMAtoMA
+from pmdarima.utils import diff
+from pmdarima.utils import diff_inv
+from pmdarima.utils import check_endog
+
+
+# Note: Originally copied from pmdarima package (https://github.com/blue-yonder/tsfresh/blob/https://github.com/alkaline-ml/pmdarima/blob/v1.8.3/pmdarima/arima/arima.py)
+def seasonal_prediction_with_confidence(arima_res,
+                                        start,
+                                        end,
+                                        X,
+                                        alpha,
+                                        **kwargs):
+    """Compute the prediction for a SARIMAX and get a conf interval
+
+    Unfortunately, SARIMAX does not really provide a nice way to get the
+    confidence intervals out of the box, so we have to perform the
+    ``get_prediction`` code here and unpack the confidence intervals manually.
+    """
+    results = arima_res.get_prediction(
+        start=start,
+        end=end,
+        exog=X,
+        **kwargs)
+
+    f = results.predicted_mean
+    conf_int = results.conf_int(alpha=alpha)
+    if arima_res.specification['simple_differencing']:
+        # If simple_differencing == True, statsmodels.get_prediction returns
+        # mid and confidence intervals on differenced time series.
+        # We have to invert differencing the mid and confidence intervals
+        y_org = arima_res.model.orig_endog
+        d = arima_res.model.orig_k_diff
+        D = arima_res.model.orig_k_seasonal_diff
+        period = arima_res.model.seasonal_periods
+        # Forecast mid: undifferencing non-seasonal part
+        if d > 0:
+            y_sdiff = y_org if D == 0 else diff(y_org, period, D)
+            f_temp = np.append(y_sdiff[-d:], f)
+            f_temp = diff_inv(f_temp, 1, d)
+            f = f_temp[(2 * d):]
+        # Forecast mid: undifferencing seasonal part
+        if D > 0 and period > 1:
+            f_temp = np.append(y_org[-(D * period):], f)
+            f_temp = diff_inv(f_temp, period, D)
+            f = f_temp[(2 * D * period):]
+        # confidence interval
+        ar_poly = arima_res.polynomial_reduced_ar
+        poly_diff = np_polynomial.polypow(np.array([1., -1.]), d)
+        sdiff = np.zeros(period + 1)
+        sdiff[0] = 1.
+        sdiff[-1] = 1.
+        poly_sdiff = np_polynomial.polypow(sdiff, D)
+        ar = -np.polymul(ar_poly, np.polymul(poly_diff, poly_sdiff))[1:]
+        ma = arima_res.polynomial_reduced_ma[1:]
+        n_predMinus1 = end - start
+        ema = ARMAtoMA(ar, ma, n_predMinus1)
+        sigma2 = arima_res._params_variance[0]
+        var = np.cumsum(np.append(1., ema * ema)) * sigma2
+        q = results.dist.ppf(1. - alpha / 2, *results.dist_args)
+        conf_int[:, 0] = f - q * np.sqrt(var)
+        conf_int[:, 1] = f + q * np.sqrt(var)
+
+    return check_endog(f, dtype=None, copy=False), \
+        check_array(conf_int, copy=False, dtype=None)
diff --git a/etna/models/sarimax.py b/etna/models/sarimax.py
index d600584e4..6ebfbcda7 100644
--- a/etna/models/sarimax.py
+++ b/etna/models/sarimax.py
@@ -6,10 +6,10 @@
 from typing import Tuple
 
 import pandas as pd
-from pmdarima.arima.arima import _seasonal_prediction_with_confidence
 from statsmodels.tools.sm_exceptions import ValueWarning
 from statsmodels.tsa.statespace.sarimax import SARIMAX
 
+from etna.libs.pmdarima import seasonal_prediction_with_confidence
 from etna.models.base import BaseAdapter
 from etna.models.base import PerSegmentPredictionIntervalModel
 from etna.models.utils import determine_num_steps
@@ -277,14 +277,14 @@ def predict(self, df: pd.DataFrame, prediction_interval: bool, quantiles: Sequen
             start_timestamp=self._first_train_timestamp, end_timestamp=end_timestamp, freq=self._freq  # type: ignore
         )
         if prediction_interval:
-            forecast, _ = _seasonal_prediction_with_confidence(
+            forecast, _ = seasonal_prediction_with_confidence(
                 arima_res=self._result, start=start_idx, end=end_idx, X=exog_future, alpha=0.05
             )
             y_pred = pd.DataFrame({"mean": forecast})
             for quantile in quantiles:
                 # set alpha in the way to get a desirable quantile
                 alpha = min(quantile * 2, (1 - quantile) * 2)
-                _, borders = _seasonal_prediction_with_confidence(
+                _, borders = seasonal_prediction_with_confidence(
                     arima_res=self._result, start=start_idx, end=end_idx, X=exog_future, alpha=alpha
                 )
                 if quantile < 1 / 2:
@@ -293,7 +293,7 @@ def predict(self, df: pd.DataFrame, prediction_interval: bool, quantiles: Sequen
                     series = borders[:, 1]
                 y_pred[f"mean_{quantile:.4g}"] = series
         else:
-            forecast, _ = _seasonal_prediction_with_confidence(
+            forecast, _ = seasonal_prediction_with_confidence(
                 arima_res=self._result, start=start_idx, end=end_idx, X=exog_future, alpha=0.05
             )
             y_pred = pd.DataFrame({"mean": forecast})

From 48b61ce711d162b48c699ea4858f358e48eb0fe2 Mon Sep 17 00:00:00 2001
From: "d.a.bunin" <d.a.bunin@tinkoff.ru>
Date: Thu, 4 Aug 2022 17:01:46 +0300
Subject: [PATCH 10/22] Fix import

---
 etna/libs/pmdarima/__init__.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/etna/libs/pmdarima/__init__.py b/etna/libs/pmdarima/__init__.py
index a04d2fad6..4e50ea79f 100644
--- a/etna/libs/pmdarima/__init__.py
+++ b/etna/libs/pmdarima/__init__.py
@@ -1 +1 @@
-from arima import seasonal_prediction_with_confidence
+from etna.libs.pmdarima.arima import seasonal_prediction_with_confidence

From bd6cc962ec83f21390ec2a6c8353ae65cb821235 Mon Sep 17 00:00:00 2001
From: "d.a.bunin" <d.a.bunin@tinkoff.ru>
Date: Thu, 4 Aug 2022 18:36:54 +0300
Subject: [PATCH 11/22] Rename file

---
 etna/libs/pmdarima/{arima.py => utils.py} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename etna/libs/pmdarima/{arima.py => utils.py} (100%)

diff --git a/etna/libs/pmdarima/arima.py b/etna/libs/pmdarima/utils.py
similarity index 100%
rename from etna/libs/pmdarima/arima.py
rename to etna/libs/pmdarima/utils.py

From 37a1ba744981504fb50dfdfcc2edecf188808046 Mon Sep 17 00:00:00 2001
From: "d.a.bunin" <d.a.bunin@tinkoff.ru>
Date: Thu, 4 Aug 2022 18:48:41 +0300
Subject: [PATCH 12/22] Rename file

---
 etna/libs/pmdarima/__init__.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/etna/libs/pmdarima/__init__.py b/etna/libs/pmdarima/__init__.py
index 4e50ea79f..27b37f0c1 100644
--- a/etna/libs/pmdarima/__init__.py
+++ b/etna/libs/pmdarima/__init__.py
@@ -1 +1 @@
-from etna.libs.pmdarima.arima import seasonal_prediction_with_confidence
+from etna.libs.pmdarima.utils import seasonal_prediction_with_confidence

From e094ae82bce41f93cc2d78a21302389b4378257d Mon Sep 17 00:00:00 2001
From: "d.a.bunin" <d.a.bunin@tinkoff.ru>
Date: Thu, 4 Aug 2022 19:14:01 +0300
Subject: [PATCH 13/22] Rename file, revert pyproject, add ARMAtoMA

---
 etna/libs/pmdarima/__init__.py            |  2 +-
 etna/libs/pmdarima/{utils.py => arima.py} | 71 ++++++++++++++++++++++-
 pyproject.toml                            |  4 +-
 3 files changed, 72 insertions(+), 5 deletions(-)
 rename etna/libs/pmdarima/{utils.py => arima.py} (62%)

diff --git a/etna/libs/pmdarima/__init__.py b/etna/libs/pmdarima/__init__.py
index 27b37f0c1..4e50ea79f 100644
--- a/etna/libs/pmdarima/__init__.py
+++ b/etna/libs/pmdarima/__init__.py
@@ -1 +1 @@
-from etna.libs.pmdarima.utils import seasonal_prediction_with_confidence
+from etna.libs.pmdarima.arima import seasonal_prediction_with_confidence
diff --git a/etna/libs/pmdarima/utils.py b/etna/libs/pmdarima/arima.py
similarity index 62%
rename from etna/libs/pmdarima/utils.py
rename to etna/libs/pmdarima/arima.py
index 371e902f3..81cdac3c8 100644
--- a/etna/libs/pmdarima/utils.py
+++ b/etna/libs/pmdarima/arima.py
@@ -25,13 +25,80 @@
 import numpy as np
 import numpy.polynomial.polynomial as np_polynomial
 from sklearn.utils.validation import check_array
-from pmdarima.arima import ARMAtoMA
 from pmdarima.utils import diff
 from pmdarima.utils import diff_inv
 from pmdarima.utils import check_endog
 
 
-# Note: Originally copied from pmdarima package (https://github.com/blue-yonder/tsfresh/blob/https://github.com/alkaline-ml/pmdarima/blob/v1.8.3/pmdarima/arima/arima.py)
+# Note: Originally copied from pmdarima package (https://github.com/blue-yonder/tsfresh/blob/https://github.com/alkaline-ml/pmdarima/blob/v1.8.5/pmdarima/arima/arima.py)
+def ARMAtoMA(ar, ma, max_deg):
+    r"""
+    Convert ARMA coefficients to infinite MA coefficients.
+    Compute coefficients of MA model equivalent to given ARMA model.
+    MA coefficients are cut off at max_deg.
+    The same function as ARMAtoMA() in stats library of R
+    Parameters
+    ----------
+    ar : array-like, shape=(n_orders,)
+        The array of AR coefficients.
+    ma : array-like, shape=(n_orders,)
+        The array of MA coefficients.
+    max_deg : int
+        Coefficients are computed up to the order of max_deg.
+    Returns
+    -------
+    np.ndarray, shape=(max_deg,)
+        Equivalent MA coefficients.
+    Notes
+    -----
+    Here is the derivation. Suppose ARMA model is defined as
+    .. math::
+    x_t - ar_1*x_{t-1} - ar_2*x_{t-2} - ... - ar_p*x_{t-p}\\
+        = e_t + ma_1*e_{t-1} + ma_2*e_{t-2} + ... + ma_q*e_{t-q}
+    namely
+    .. math::
+    (1 - \sum_{i=1}^p[ar_i*B^i]) x_t = (1 + \sum_{i=1}^q[ma_i*B^i]) e_t
+    where :math:`B` is a backward operator.
+    Equivalent MA model is
+    .. math::
+        x_t = (1 - \sum_{i=1}^p[ar_i*B^i])^{-1}\\
+        * (1 + \sum_{i=1}^q[ma_i*B^i]) e_t\\
+        = (1 + \sum_{i=1}[ema_i*B^i]) e_t
+    where :math:``ema_i`` is a coefficient of equivalent MA model.
+    The :math:``ema_i`` satisfies
+    .. math::
+        (1 - \sum_{i=1}^p[ar_i*B^i]) * (1 + \sum_{i=1}[ema_i*B^i]) \\
+        = 1 + \sum_{i=1}^q[ma_i*B^i]
+    thus
+    .. math::
+        \sum_{i=1}[ema_i*B^i] = \sum_{i=1}^p[ar_i*B^i] \\
+        + \sum_{i=1}^p[ar_i*B^i] * \sum_{j=1}[ema_j*B^j] \\
+        + \Sum_{i=1}^q[ma_i*B^i]
+    therefore
+    .. math::
+        ema_i = ar_i (but 0 if i>p) \\
+        + \Sum_{j=1}^{min(i-1,p)}[ar_j*ema_{i-j}] + ma_i(but 0 if i>q) \\
+        = \sum_{j=1}{min(i,p)}[ar_j*ema_{i-j}(but 1 if j=i)] \\
+        + ma_i(but 0 if i>q)
+    Examples
+    --------
+    >>> ar = np.array([0.1])
+    >>> ma = np.empty(0)
+    >>> ARMAtoMA(ar, ma, 3)
+    array[0.1, 0.01, 0.001]
+    """
+    p = len(ar)
+    q = len(ma)
+    ema = np.empty(max_deg)
+    for i in range(0, max_deg):
+        temp = ma[i] if i < q else 0.0
+        for j in range(0, min(i + 1, p)):
+            temp += ar[j] * (ema[i - j - 1] if i - j - 1 >= 0 else 1.0)
+        ema[i] = temp
+    return ema
+
+
+# Note: Originally copied from pmdarima package (https://github.com/blue-yonder/tsfresh/blob/https://github.com/alkaline-ml/pmdarima/blob/v1.8.5/pmdarima/arima/arima.py)
 def seasonal_prediction_with_confidence(arima_res,
                                         start,
                                         end,
diff --git a/pyproject.toml b/pyproject.toml
index 8977f5a98..c1973416b 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "etna"
-version = "1.11.1"
+version = "1.11.0"
 repository = "https://github.com/tinkoff-ai/etna"
 readme = "README.md"
 description = "ETNA is the first python open source framework of Tinkoff.ru AI Center. It is designed to make working with time series simple, productive, and fun."
@@ -49,7 +49,7 @@ ruptures = "1.1.5"
 numba = ">=0.53.1,<0.56.0"
 seaborn = "^0.11.1"
 statsmodels = ">=0.12,<0.14"
-pmdarima = ">=1.8.3"
+pmdarima = ">=1.8.0"
 dill = "^0.3.4"
 toml = "^0.10.2"
 loguru = "^0.5.3"

From 7d0ab413ceb4d45fae4de5ae07d0af0962a03329 Mon Sep 17 00:00:00 2001
From: "d.a.bunin" <d.a.bunin@tinkoff.ru>
Date: Thu, 4 Aug 2022 19:15:40 +0300
Subject: [PATCH 14/22] Make more clear statement about copying

---
 etna/libs/pmdarima/arima.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/etna/libs/pmdarima/arima.py b/etna/libs/pmdarima/arima.py
index 81cdac3c8..e05031d00 100644
--- a/etna/libs/pmdarima/arima.py
+++ b/etna/libs/pmdarima/arima.py
@@ -21,6 +21,7 @@
 OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 SOFTWARE.
 """
+# Note: Copied from pmdarima package (https://github.com/blue-yonder/tsfresh/blob/https://github.com/alkaline-ml/pmdarima/blob/v1.8.5/pmdarima/arima/arima.py)
 
 import numpy as np
 import numpy.polynomial.polynomial as np_polynomial
@@ -30,7 +31,6 @@
 from pmdarima.utils import check_endog
 
 
-# Note: Originally copied from pmdarima package (https://github.com/blue-yonder/tsfresh/blob/https://github.com/alkaline-ml/pmdarima/blob/v1.8.5/pmdarima/arima/arima.py)
 def ARMAtoMA(ar, ma, max_deg):
     r"""
     Convert ARMA coefficients to infinite MA coefficients.

From fdaa4a5fc6490eeab0e74300110563e289d62d60 Mon Sep 17 00:00:00 2001
From: "d.a.bunin" <d.a.bunin@tinkoff.ru>
Date: Thu, 4 Aug 2022 19:37:38 +0300
Subject: [PATCH 15/22] Fix bug with version

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index c1973416b..abaf7987f 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "etna"
-version = "1.11.0"
+version = "1.11.1"
 repository = "https://github.com/tinkoff-ai/etna"
 readme = "README.md"
 description = "ETNA is the first python open source framework of Tinkoff.ru AI Center. It is designed to make working with time series simple, productive, and fun."

From c05c42f8b9c2b3e4fe9ef2df0a6177a46e6a841f Mon Sep 17 00:00:00 2001
From: "d.a.bunin" <d.a.bunin@tinkoff.ru>
Date: Fri, 5 Aug 2022 10:35:13 +0300
Subject: [PATCH 16/22] Change import

---
 etna/models/sarimax.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/etna/models/sarimax.py b/etna/models/sarimax.py
index 6ebfbcda7..7d6a68ede 100644
--- a/etna/models/sarimax.py
+++ b/etna/models/sarimax.py
@@ -9,7 +9,7 @@
 from statsmodels.tools.sm_exceptions import ValueWarning
 from statsmodels.tsa.statespace.sarimax import SARIMAX
 
-from etna.libs.pmdarima import seasonal_prediction_with_confidence
+from etna.libs.pmdarima.arima import seasonal_prediction_with_confidence
 from etna.models.base import BaseAdapter
 from etna.models.base import PerSegmentPredictionIntervalModel
 from etna.models.utils import determine_num_steps

From 7c6cb6bc4d6a9d4dbed5a8a8f1b0f34cad885ebc Mon Sep 17 00:00:00 2001
From: "d.a.bunin" <d.a.bunin@tinkoff.ru>
Date: Fri, 5 Aug 2022 11:02:14 +0300
Subject: [PATCH 17/22] Update lock

---
 poetry.lock | 1628 +++++----------------------------------------------
 1 file changed, 143 insertions(+), 1485 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 7262fa919..4d83a5781 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -413,9 +413,9 @@ tqdm = "*"
 ujson = "*"
 
 [package.extras]
+tests = ["types-ujson", "xarray", "tqdm", "testfixtures", "mypy", "pytest-order", "pytest-cov", "pytest", "pylint", "flake8"]
+docs = ["matplotlib", "numpydoc", "sphinx-rtd-theme", "sphinx-gallery", "sphinx"]
 all = ["xarray"]
-docs = ["sphinx", "sphinx-gallery", "sphinx-rtd-theme", "numpydoc", "matplotlib"]
-tests = ["flake8", "pylint", "pytest", "pytest-cov", "pytest-order", "mypy", "testfixtures", "tqdm", "xarray", "types-ujson"]
 
 [[package]]
 name = "codespell"
@@ -463,9 +463,9 @@ python-versions = "<4,>=3.7"
 pymeeus = ">=0.3.13,<=1"
 
 [package.extras]
-dev = ["build", "black", "isort", "pylint"]
-docs = ["sphinx", "sphinx-rtd-theme", "myst-parser"]
 tests = ["coverage"]
+docs = ["myst-parser", "sphinx-rtd-theme", "sphinx"]
+dev = ["pylint", "isort", "black", "build"]
 
 [[package]]
 name = "coverage"
@@ -685,7 +685,7 @@ python-versions = ">=3.7"
 
 [[package]]
 name = "fsspec"
-version = "2022.5.0"
+version = "2022.7.1"
 description = "File-system specification"
 category = "main"
 optional = true
@@ -1534,7 +1534,7 @@ Jinja2 = ">=2.10"
 sphinx = ">=3.0"
 
 [package.extras]
-testing = ["pytest", "pytest-cov", "matplotlib"]
+testing = ["matplotlib", "pytest-cov", "pytest"]
 
 [[package]]
 name = "oauthlib"
@@ -2187,7 +2187,7 @@ python-versions = ">=3.7"
 packaging = "*"
 
 [package.extras]
-test = ["pytest (>=6,!=7.0.0,!=7.0.1)", "pytest-cov (>=3.0.0)", "pytest-qt"]
+test = ["pytest-qt", "pytest-cov (>=3.0.0)", "pytest (>=6,!=7.0.0,!=7.0.1)"]
 
 [[package]]
 name = "requests"
@@ -2280,10 +2280,10 @@ scipy = ">=1.1.0"
 threadpoolctl = ">=2.0.0"
 
 [package.extras]
-benchmark = ["matplotlib (>=2.2.3)", "pandas (>=0.25.0)", "memory-profiler (>=0.57.0)"]
-docs = ["matplotlib (>=2.2.3)", "scikit-image (>=0.14.5)", "pandas (>=0.25.0)", "seaborn (>=0.9.0)", "memory-profiler (>=0.57.0)", "sphinx (>=4.0.1)", "sphinx-gallery (>=0.7.0)", "numpydoc (>=1.0.0)", "Pillow (>=7.1.2)", "sphinx-prompt (>=1.3.0)", "sphinxext-opengraph (>=0.4.2)"]
-examples = ["matplotlib (>=2.2.3)", "scikit-image (>=0.14.5)", "pandas (>=0.25.0)", "seaborn (>=0.9.0)"]
-tests = ["matplotlib (>=2.2.3)", "scikit-image (>=0.14.5)", "pandas (>=0.25.0)", "pytest (>=5.0.1)", "pytest-cov (>=2.9.0)", "flake8 (>=3.8.2)", "black (>=21.6b0)", "mypy (>=0.770)", "pyamg (>=4.0.0)"]
+tests = ["pyamg (>=4.0.0)", "mypy (>=0.770)", "black (>=21.6b0)", "flake8 (>=3.8.2)", "pytest-cov (>=2.9.0)", "pytest (>=5.0.1)", "pandas (>=0.25.0)", "scikit-image (>=0.14.5)", "matplotlib (>=2.2.3)"]
+examples = ["seaborn (>=0.9.0)", "pandas (>=0.25.0)", "scikit-image (>=0.14.5)", "matplotlib (>=2.2.3)"]
+docs = ["sphinxext-opengraph (>=0.4.2)", "sphinx-prompt (>=1.3.0)", "Pillow (>=7.1.2)", "numpydoc (>=1.0.0)", "sphinx-gallery (>=0.7.0)", "sphinx (>=4.0.1)", "memory-profiler (>=0.57.0)", "seaborn (>=0.9.0)", "pandas (>=0.25.0)", "scikit-image (>=0.14.5)", "matplotlib (>=2.2.3)"]
+benchmark = ["memory-profiler (>=0.57.0)", "pandas (>=0.25.0)", "matplotlib (>=2.2.3)"]
 
 [[package]]
 name = "scipy"
@@ -2647,7 +2647,7 @@ scikit-learn = "*"
 scipy = "*"
 
 [package.extras]
-dev = ["pip-tools", "rpy2"]
+dev = ["rpy2", "pip-tools"]
 
 [[package]]
 name = "tenacity"
@@ -2733,8 +2733,8 @@ python-versions = ">=3.6"
 webencodings = ">=0.4"
 
 [package.extras]
-doc = ["sphinx", "sphinx-rtd-theme"]
-test = ["pytest", "pytest-cov", "pytest-flake8", "pytest-isort", "coverage"]
+test = ["coverage", "pytest-isort", "pytest-flake8", "pytest-cov", "pytest"]
+doc = ["sphinx-rtd-theme", "sphinx"]
 
 [[package]]
 name = "tokenize-rt"
@@ -2786,14 +2786,14 @@ torch = ">=1.3.1"
 typing-extensions = {version = "*", markers = "python_version < \"3.8\""}
 
 [package.extras]
-all = ["pystoi", "torchvision (>=0.8)", "pycocotools", "sphinx-copybutton (>=0.3)", "nbsphinx (>=0.8)", "docutils (>=0.16)", "sphinxcontrib-fulltoc (>=1.0)", "sphinx-paramlinks (>=0.5.1)", "sphinx-autodoc-typehints (>=1.0)", "sphinx (>=4.0,<5.0)", "myst-parser", "sphinx-togglebutton (>=0.2)", "pandoc (>=1.0)", "sphinxcontrib-mockautodoc", "torchvision", "torch-fidelity", "lpips", "scipy", "pytorch-lightning (>=1.5)", "mypy (>=0.790)", "transformers (>=4.0)", "pytorch-msssim", "scikit-image (>0.17.1)", "fast-bss-eval (>=0.1.0)", "rouge-score (>=0.0.4)", "torch-complex", "bert-score (==0.3.10)", "cloudpickle (>=1.3)", "jiwer (>=2.3.0)", "pypesq", "coverage (>5.2)", "codecov (>=2.1)", "huggingface-hub (<0.7)", "sacrebleu (>=2.0.0)", "pytest-cov (>2.10)", "psutil", "pre-commit (>=1.0)", "requests", "pytest (>=6.0.0,<7.0.0)", "twine (>=3.2)", "scikit-learn (>1.0,<1.1.1)", "pytest-doctestplus (>=0.9.0)", "pytest-timeout", "phmdoctest (>=1.1.1)", "check-manifest", "mir-eval (>=0.6)", "fire", "nltk (>=3.6)", "regex (>=2021.9.24)", "tqdm (>=4.41.0)"]
-audio = ["pystoi"]
-detection = ["torchvision (>=0.8)", "pycocotools"]
-docs = ["sphinx-copybutton (>=0.3)", "nbsphinx (>=0.8)", "docutils (>=0.16)", "sphinxcontrib-fulltoc (>=1.0)", "sphinx-paramlinks (>=0.5.1)", "sphinx-autodoc-typehints (>=1.0)", "sphinx (>=4.0,<5.0)", "myst-parser", "sphinx-togglebutton (>=0.2)", "pandoc (>=1.0)", "sphinxcontrib-mockautodoc"]
-image = ["torchvision", "torch-fidelity", "lpips", "scipy"]
+text = ["tqdm (>=4.41.0)", "regex (>=2021.9.24)", "nltk (>=3.6)"]
+test = ["fire", "mir-eval (>=0.6)", "check-manifest", "phmdoctest (>=1.1.1)", "pytest-timeout", "pytest-doctestplus (>=0.9.0)", "scikit-learn (>1.0,<1.1.1)", "twine (>=3.2)", "pytest (>=6.0.0,<7.0.0)", "requests", "pre-commit (>=1.0)", "psutil", "pytest-cov (>2.10)", "sacrebleu (>=2.0.0)", "huggingface-hub (<0.7)", "codecov (>=2.1)", "coverage (>5.2)", "pypesq", "jiwer (>=2.3.0)", "cloudpickle (>=1.3)", "bert-score (==0.3.10)", "torch-complex", "rouge-score (>=0.0.4)", "pycocotools", "fast-bss-eval (>=0.1.0)", "scikit-image (>0.17.1)", "pytorch-msssim", "transformers (>=4.0)", "mypy (>=0.790)"]
 integrate = ["pytorch-lightning (>=1.5)"]
-test = ["mypy (>=0.790)", "transformers (>=4.0)", "pytorch-msssim", "scikit-image (>0.17.1)", "fast-bss-eval (>=0.1.0)", "pycocotools", "rouge-score (>=0.0.4)", "torch-complex", "bert-score (==0.3.10)", "cloudpickle (>=1.3)", "jiwer (>=2.3.0)", "pypesq", "coverage (>5.2)", "codecov (>=2.1)", "huggingface-hub (<0.7)", "sacrebleu (>=2.0.0)", "pytest-cov (>2.10)", "psutil", "pre-commit (>=1.0)", "requests", "pytest (>=6.0.0,<7.0.0)", "twine (>=3.2)", "scikit-learn (>1.0,<1.1.1)", "pytest-doctestplus (>=0.9.0)", "pytest-timeout", "phmdoctest (>=1.1.1)", "check-manifest", "mir-eval (>=0.6)", "fire"]
-text = ["nltk (>=3.6)", "regex (>=2021.9.24)", "tqdm (>=4.41.0)"]
+image = ["scipy", "lpips", "torch-fidelity", "torchvision"]
+docs = ["sphinxcontrib-mockautodoc", "pandoc (>=1.0)", "sphinx-togglebutton (>=0.2)", "myst-parser", "sphinx (>=4.0,<5.0)", "sphinx-autodoc-typehints (>=1.0)", "sphinx-paramlinks (>=0.5.1)", "sphinxcontrib-fulltoc (>=1.0)", "docutils (>=0.16)", "nbsphinx (>=0.8)", "sphinx-copybutton (>=0.3)"]
+detection = ["pycocotools", "torchvision (>=0.8)"]
+audio = ["pystoi"]
+all = ["tqdm (>=4.41.0)", "regex (>=2021.9.24)", "nltk (>=3.6)", "fire", "mir-eval (>=0.6)", "check-manifest", "phmdoctest (>=1.1.1)", "pytest-timeout", "pytest-doctestplus (>=0.9.0)", "scikit-learn (>1.0,<1.1.1)", "twine (>=3.2)", "pytest (>=6.0.0,<7.0.0)", "requests", "pre-commit (>=1.0)", "psutil", "pytest-cov (>2.10)", "sacrebleu (>=2.0.0)", "huggingface-hub (<0.7)", "codecov (>=2.1)", "coverage (>5.2)", "pypesq", "jiwer (>=2.3.0)", "cloudpickle (>=1.3)", "bert-score (==0.3.10)", "torch-complex", "rouge-score (>=0.0.4)", "fast-bss-eval (>=0.1.0)", "scikit-image (>0.17.1)", "pytorch-msssim", "transformers (>=4.0)", "mypy (>=0.790)", "pytorch-lightning (>=1.5)", "scipy", "lpips", "torch-fidelity", "torchvision", "sphinxcontrib-mockautodoc", "pandoc (>=1.0)", "sphinx-togglebutton (>=0.2)", "myst-parser", "sphinx (>=4.0,<5.0)", "sphinx-autodoc-typehints (>=1.0)", "sphinx-paramlinks (>=0.5.1)", "sphinxcontrib-fulltoc (>=1.0)", "docutils (>=0.16)", "nbsphinx (>=0.8)", "sphinx-copybutton (>=0.3)", "pycocotools", "torchvision (>=0.8)", "pystoi"]
 
 [[package]]
 name = "tornado"
@@ -2851,10 +2851,10 @@ python-versions = ">=3.6"
 click = ">=7.1.1,<9.0.0"
 
 [package.extras]
-all = ["colorama (>=0.4.3,<0.5.0)", "shellingham (>=1.3.0,<2.0.0)"]
-dev = ["autoflake (>=1.3.1,<2.0.0)", "flake8 (>=3.8.3,<4.0.0)", "pre-commit (>=2.17.0,<3.0.0)"]
-doc = ["mkdocs (>=1.1.2,<2.0.0)", "mkdocs-material (>=8.1.4,<9.0.0)", "mdx-include (>=1.4.1,<2.0.0)"]
-test = ["shellingham (>=1.3.0,<2.0.0)", "pytest (>=4.4.0,<5.4.0)", "pytest-cov (>=2.10.0,<3.0.0)", "coverage (>=5.2,<6.0)", "pytest-xdist (>=1.32.0,<2.0.0)", "pytest-sugar (>=0.9.4,<0.10.0)", "mypy (==0.910)", "black (>=22.3.0,<23.0.0)", "isort (>=5.0.6,<6.0.0)"]
+test = ["isort (>=5.0.6,<6.0.0)", "black (>=22.3.0,<23.0.0)", "mypy (==0.910)", "pytest-sugar (>=0.9.4,<0.10.0)", "pytest-xdist (>=1.32.0,<2.0.0)", "coverage (>=5.2,<6.0)", "pytest-cov (>=2.10.0,<3.0.0)", "pytest (>=4.4.0,<5.4.0)", "shellingham (>=1.3.0,<2.0.0)"]
+doc = ["mdx-include (>=1.4.1,<2.0.0)", "mkdocs-material (>=8.1.4,<9.0.0)", "mkdocs (>=1.1.2,<2.0.0)"]
+dev = ["pre-commit (>=2.17.0,<3.0.0)", "flake8 (>=3.8.3,<4.0.0)", "autoflake (>=1.3.1,<2.0.0)"]
+all = ["shellingham (>=1.3.0,<2.0.0)", "colorama (>=0.4.3,<0.5.0)"]
 
 [[package]]
 name = "types-deprecated"
@@ -2982,7 +2982,7 @@ optional = false
 python-versions = ">=3.5"
 
 [package.extras]
-dev = ["pytest (>=4.6.2)", "black (>=19.3b0)"]
+dev = ["black (>=19.3b0)", "pytest (>=4.6.2)"]
 
 [[package]]
 name = "wrapt"
@@ -3035,10 +3035,7 @@ python-versions = ">=3.7.1, <3.10.0"
 content-hash = "6e059f258cfc27d3f7901c6d14e7ef7d2ff9acc25563c40cda1bf2601d38e7ac"
 
 [metadata.files]
-absl-py = [
-    {file = "absl-py-1.1.0.tar.gz", hash = "sha256:3aa39f898329c2156ff525dfa69ce709e42d77aab18bf4917719d6f260aa6a08"},
-    {file = "absl_py-1.1.0-py3-none-any.whl", hash = "sha256:db97287655e30336938f8058d2c81ed2be6af1d9b6ebbcd8df1080a6c7fcd24e"},
-]
+absl-py = []
 aiohttp = [
     {file = "aiohttp-3.8.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:1ed0b6477896559f17b9eaeb6d38e07f7f9ffe40b9f0f9627ae8b9926ae260a8"},
     {file = "aiohttp-3.8.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:7dadf3c307b31e0e61689cbf9e06be7a867c563d5a63ce9dca578f956609abf8"},
@@ -3122,13 +3119,8 @@ alabaster = [
     {file = "alabaster-0.7.12.tar.gz", hash = "sha256:a661d72d58e6ea8a57f7a86e37d86716863ee5e92788398526d58b26a4e4dc02"},
 ]
 alembic = []
-antlr4-python3-runtime = [
-    {file = "antlr4-python3-runtime-4.9.3.tar.gz", hash = "sha256:f224469b4168294902bb1efa80a8bf7855f24c99aef99cbefc1bcd3cce77881b"},
-]
-appnope = [
-    {file = "appnope-0.1.3-py2.py3-none-any.whl", hash = "sha256:265a455292d0bd8a72453494fa24df5a11eb18373a60c7c0430889f22548605e"},
-    {file = "appnope-0.1.3.tar.gz", hash = "sha256:02bd91c4de869fbb1e1c50aafc4098827a7a54ab2f39d9dcba6c9547ed920e24"},
-]
+antlr4-python3-runtime = []
+appnope = []
 argon2-cffi = [
     {file = "argon2-cffi-21.3.0.tar.gz", hash = "sha256:d384164d944190a7dd7ef22c6aa3ff197da12962bd04b17f64d4e93d934dba5b"},
     {file = "argon2_cffi-21.3.0-py3-none-any.whl", hash = "sha256:8c976986f2c5c0e5000919e6de187906cfd81fb1c72bf9d88c01177e77da7f80"},
@@ -3156,207 +3148,32 @@ argon2-cffi-bindings = [
     {file = "argon2_cffi_bindings-21.2.0-pp38-pypy38_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ed2937d286e2ad0cc79a7087d3c272832865f779430e0cc2b4f3718d3159b0cb"},
     {file = "argon2_cffi_bindings-21.2.0-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:5e00316dabdaea0b2dd82d141cc66889ced0cdcbfa599e8b471cf22c620c329a"},
 ]
-async-timeout = [
-    {file = "async-timeout-4.0.2.tar.gz", hash = "sha256:2163e1640ddb52b7a8c80d0a67a08587e5d245cc9c553a74a847056bc2976b15"},
-    {file = "async_timeout-4.0.2-py3-none-any.whl", hash = "sha256:8ca1e4fcf50d07413d66d1a5e416e42cfdf5851c981d679a09851a6853383b3c"},
-]
+async-timeout = []
 asynctest = [
     {file = "asynctest-0.13.0-py3-none-any.whl", hash = "sha256:5da6118a7e6d6b54d83a8f7197769d046922a44d2a99c21382f0a6e4fadae676"},
     {file = "asynctest-0.13.0.tar.gz", hash = "sha256:c27862842d15d83e6a34eb0b2866c323880eb3a75e4485b079ea11748fd77fac"},
 ]
 atomicwrites = []
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-    {file = "ujson-5.4.0-pp38-pypy38_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ef985eb2770900a485431910bd3f333b56d1a34b65f8c26a6ed8e8adf55f98d9"},
-    {file = "ujson-5.4.0-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:025758cf6561af6986d77cd4af9367ab56dde5c7c50f13f59e6964b4b25df73e"},
-    {file = "ujson-5.4.0.tar.gz", hash = "sha256:6b953e09441e307504130755e5bd6b15850178d591f66292bba4608c4f7f9b00"},
-]
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+ujson = []
 urllib3 = []
 wandb = []
 wcwidth = [
@@ -5257,18 +3924,9 @@ webencodings = [
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     {file = "webencodings-0.5.1.tar.gz", hash = "sha256:b36a1c245f2d304965eb4e0a82848379241dc04b865afcc4aab16748587e1923"},
 ]
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-    {file = "Werkzeug-2.1.2.tar.gz", hash = "sha256:1ce08e8093ed67d638d63879fd1ba3735817f7a80de3674d293f5984f25fb6e6"},
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+widgetsnbextension = []
+win32-setctime = []
 wrapt = []
 yarl = [
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From 9ea9518a9d3ef293b9cf39ea626549ec09faaf2d Mon Sep 17 00:00:00 2001
From: "d.a.bunin" <d.a.bunin@tinkoff.ru>
Date: Fri, 5 Aug 2022 11:27:29 +0300
Subject: [PATCH 18/22] Fix module name

---
 etna/libs/pmdarima/__init__.py                  | 1 -
 etna/libs/pmdarima_utils/__init__.py            | 1 +
 etna/libs/{pmdarima => pmdarima_utils}/arima.py | 0
 etna/models/sarimax.py                          | 2 +-
 4 files changed, 2 insertions(+), 2 deletions(-)
 delete mode 100644 etna/libs/pmdarima/__init__.py
 create mode 100644 etna/libs/pmdarima_utils/__init__.py
 rename etna/libs/{pmdarima => pmdarima_utils}/arima.py (100%)

diff --git a/etna/libs/pmdarima/__init__.py b/etna/libs/pmdarima/__init__.py
deleted file mode 100644
index 4e50ea79f..000000000
--- a/etna/libs/pmdarima/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from etna.libs.pmdarima.arima import seasonal_prediction_with_confidence
diff --git a/etna/libs/pmdarima_utils/__init__.py b/etna/libs/pmdarima_utils/__init__.py
new file mode 100644
index 000000000..bff45169f
--- /dev/null
+++ b/etna/libs/pmdarima_utils/__init__.py
@@ -0,0 +1 @@
+from etna.libs.pmdarima_utils.arima import seasonal_prediction_with_confidence
diff --git a/etna/libs/pmdarima/arima.py b/etna/libs/pmdarima_utils/arima.py
similarity index 100%
rename from etna/libs/pmdarima/arima.py
rename to etna/libs/pmdarima_utils/arima.py
diff --git a/etna/models/sarimax.py b/etna/models/sarimax.py
index 7d6a68ede..46cc30ce7 100644
--- a/etna/models/sarimax.py
+++ b/etna/models/sarimax.py
@@ -9,7 +9,7 @@
 from statsmodels.tools.sm_exceptions import ValueWarning
 from statsmodels.tsa.statespace.sarimax import SARIMAX
 
-from etna.libs.pmdarima.arima import seasonal_prediction_with_confidence
+from etna.libs.pmdarima_utils import seasonal_prediction_with_confidence
 from etna.models.base import BaseAdapter
 from etna.models.base import PerSegmentPredictionIntervalModel
 from etna.models.utils import determine_num_steps

From 31c5dffde7df296e275448221c551b4a3ed9d503 Mon Sep 17 00:00:00 2001
From: "d.a.bunin" <d.a.bunin@tinkoff.ru>
Date: Fri, 5 Aug 2022 11:29:04 +0300
Subject: [PATCH 19/22] Revert "Update lock"

This reverts commit 7c6cb6bc4d6a9d4dbed5a8a8f1b0f34cad885ebc.
---
 poetry.lock | 1628 ++++++++++++++++++++++++++++++++++++++++++++++-----
 1 file changed, 1485 insertions(+), 143 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 4d83a5781..7262fa919 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -413,9 +413,9 @@ tqdm = "*"
 ujson = "*"
 
 [package.extras]
-tests = ["types-ujson", "xarray", "tqdm", "testfixtures", "mypy", "pytest-order", "pytest-cov", "pytest", "pylint", "flake8"]
-docs = ["matplotlib", "numpydoc", "sphinx-rtd-theme", "sphinx-gallery", "sphinx"]
 all = ["xarray"]
+docs = ["sphinx", "sphinx-gallery", "sphinx-rtd-theme", "numpydoc", "matplotlib"]
+tests = ["flake8", "pylint", "pytest", "pytest-cov", "pytest-order", "mypy", "testfixtures", "tqdm", "xarray", "types-ujson"]
 
 [[package]]
 name = "codespell"
@@ -463,9 +463,9 @@ python-versions = "<4,>=3.7"
 pymeeus = ">=0.3.13,<=1"
 
 [package.extras]
+dev = ["build", "black", "isort", "pylint"]
+docs = ["sphinx", "sphinx-rtd-theme", "myst-parser"]
 tests = ["coverage"]
-docs = ["myst-parser", "sphinx-rtd-theme", "sphinx"]
-dev = ["pylint", "isort", "black", "build"]
 
 [[package]]
 name = "coverage"
@@ -685,7 +685,7 @@ python-versions = ">=3.7"
 
 [[package]]
 name = "fsspec"
-version = "2022.7.1"
+version = "2022.5.0"
 description = "File-system specification"
 category = "main"
 optional = true
@@ -1534,7 +1534,7 @@ Jinja2 = ">=2.10"
 sphinx = ">=3.0"
 
 [package.extras]
-testing = ["matplotlib", "pytest-cov", "pytest"]
+testing = ["pytest", "pytest-cov", "matplotlib"]
 
 [[package]]
 name = "oauthlib"
@@ -2187,7 +2187,7 @@ python-versions = ">=3.7"
 packaging = "*"
 
 [package.extras]
-test = ["pytest-qt", "pytest-cov (>=3.0.0)", "pytest (>=6,!=7.0.0,!=7.0.1)"]
+test = ["pytest (>=6,!=7.0.0,!=7.0.1)", "pytest-cov (>=3.0.0)", "pytest-qt"]
 
 [[package]]
 name = "requests"
@@ -2280,10 +2280,10 @@ scipy = ">=1.1.0"
 threadpoolctl = ">=2.0.0"
 
 [package.extras]
-tests = ["pyamg (>=4.0.0)", "mypy (>=0.770)", "black (>=21.6b0)", "flake8 (>=3.8.2)", "pytest-cov (>=2.9.0)", "pytest (>=5.0.1)", "pandas (>=0.25.0)", "scikit-image (>=0.14.5)", "matplotlib (>=2.2.3)"]
-examples = ["seaborn (>=0.9.0)", "pandas (>=0.25.0)", "scikit-image (>=0.14.5)", "matplotlib (>=2.2.3)"]
-docs = ["sphinxext-opengraph (>=0.4.2)", "sphinx-prompt (>=1.3.0)", "Pillow (>=7.1.2)", "numpydoc (>=1.0.0)", "sphinx-gallery (>=0.7.0)", "sphinx (>=4.0.1)", "memory-profiler (>=0.57.0)", "seaborn (>=0.9.0)", "pandas (>=0.25.0)", "scikit-image (>=0.14.5)", "matplotlib (>=2.2.3)"]
-benchmark = ["memory-profiler (>=0.57.0)", "pandas (>=0.25.0)", "matplotlib (>=2.2.3)"]
+benchmark = ["matplotlib (>=2.2.3)", "pandas (>=0.25.0)", "memory-profiler (>=0.57.0)"]
+docs = ["matplotlib (>=2.2.3)", "scikit-image (>=0.14.5)", "pandas (>=0.25.0)", "seaborn (>=0.9.0)", "memory-profiler (>=0.57.0)", "sphinx (>=4.0.1)", "sphinx-gallery (>=0.7.0)", "numpydoc (>=1.0.0)", "Pillow (>=7.1.2)", "sphinx-prompt (>=1.3.0)", "sphinxext-opengraph (>=0.4.2)"]
+examples = ["matplotlib (>=2.2.3)", "scikit-image (>=0.14.5)", "pandas (>=0.25.0)", "seaborn (>=0.9.0)"]
+tests = ["matplotlib (>=2.2.3)", "scikit-image (>=0.14.5)", "pandas (>=0.25.0)", "pytest (>=5.0.1)", "pytest-cov (>=2.9.0)", "flake8 (>=3.8.2)", "black (>=21.6b0)", "mypy (>=0.770)", "pyamg (>=4.0.0)"]
 
 [[package]]
 name = "scipy"
@@ -2647,7 +2647,7 @@ scikit-learn = "*"
 scipy = "*"
 
 [package.extras]
-dev = ["rpy2", "pip-tools"]
+dev = ["pip-tools", "rpy2"]
 
 [[package]]
 name = "tenacity"
@@ -2733,8 +2733,8 @@ python-versions = ">=3.6"
 webencodings = ">=0.4"
 
 [package.extras]
-test = ["coverage", "pytest-isort", "pytest-flake8", "pytest-cov", "pytest"]
-doc = ["sphinx-rtd-theme", "sphinx"]
+doc = ["sphinx", "sphinx-rtd-theme"]
+test = ["pytest", "pytest-cov", "pytest-flake8", "pytest-isort", "coverage"]
 
 [[package]]
 name = "tokenize-rt"
@@ -2786,14 +2786,14 @@ torch = ">=1.3.1"
 typing-extensions = {version = "*", markers = "python_version < \"3.8\""}
 
 [package.extras]
-text = ["tqdm (>=4.41.0)", "regex (>=2021.9.24)", "nltk (>=3.6)"]
-test = ["fire", "mir-eval (>=0.6)", "check-manifest", "phmdoctest (>=1.1.1)", "pytest-timeout", "pytest-doctestplus (>=0.9.0)", "scikit-learn (>1.0,<1.1.1)", "twine (>=3.2)", "pytest (>=6.0.0,<7.0.0)", "requests", "pre-commit (>=1.0)", "psutil", "pytest-cov (>2.10)", "sacrebleu (>=2.0.0)", "huggingface-hub (<0.7)", "codecov (>=2.1)", "coverage (>5.2)", "pypesq", "jiwer (>=2.3.0)", "cloudpickle (>=1.3)", "bert-score (==0.3.10)", "torch-complex", "rouge-score (>=0.0.4)", "pycocotools", "fast-bss-eval (>=0.1.0)", "scikit-image (>0.17.1)", "pytorch-msssim", "transformers (>=4.0)", "mypy (>=0.790)"]
-integrate = ["pytorch-lightning (>=1.5)"]
-image = ["scipy", "lpips", "torch-fidelity", "torchvision"]
-docs = ["sphinxcontrib-mockautodoc", "pandoc (>=1.0)", "sphinx-togglebutton (>=0.2)", "myst-parser", "sphinx (>=4.0,<5.0)", "sphinx-autodoc-typehints (>=1.0)", "sphinx-paramlinks (>=0.5.1)", "sphinxcontrib-fulltoc (>=1.0)", "docutils (>=0.16)", "nbsphinx (>=0.8)", "sphinx-copybutton (>=0.3)"]
-detection = ["pycocotools", "torchvision (>=0.8)"]
+all = ["pystoi", "torchvision (>=0.8)", "pycocotools", "sphinx-copybutton (>=0.3)", "nbsphinx (>=0.8)", "docutils (>=0.16)", "sphinxcontrib-fulltoc (>=1.0)", "sphinx-paramlinks (>=0.5.1)", "sphinx-autodoc-typehints (>=1.0)", "sphinx (>=4.0,<5.0)", "myst-parser", "sphinx-togglebutton (>=0.2)", "pandoc (>=1.0)", "sphinxcontrib-mockautodoc", "torchvision", "torch-fidelity", "lpips", "scipy", "pytorch-lightning (>=1.5)", "mypy (>=0.790)", "transformers (>=4.0)", "pytorch-msssim", "scikit-image (>0.17.1)", "fast-bss-eval (>=0.1.0)", "rouge-score (>=0.0.4)", "torch-complex", "bert-score (==0.3.10)", "cloudpickle (>=1.3)", "jiwer (>=2.3.0)", "pypesq", "coverage (>5.2)", "codecov (>=2.1)", "huggingface-hub (<0.7)", "sacrebleu (>=2.0.0)", "pytest-cov (>2.10)", "psutil", "pre-commit (>=1.0)", "requests", "pytest (>=6.0.0,<7.0.0)", "twine (>=3.2)", "scikit-learn (>1.0,<1.1.1)", "pytest-doctestplus (>=0.9.0)", "pytest-timeout", "phmdoctest (>=1.1.1)", "check-manifest", "mir-eval (>=0.6)", "fire", "nltk (>=3.6)", "regex (>=2021.9.24)", "tqdm (>=4.41.0)"]
 audio = ["pystoi"]
-all = ["tqdm (>=4.41.0)", "regex (>=2021.9.24)", "nltk (>=3.6)", "fire", "mir-eval (>=0.6)", "check-manifest", "phmdoctest (>=1.1.1)", "pytest-timeout", "pytest-doctestplus (>=0.9.0)", "scikit-learn (>1.0,<1.1.1)", "twine (>=3.2)", "pytest (>=6.0.0,<7.0.0)", "requests", "pre-commit (>=1.0)", "psutil", "pytest-cov (>2.10)", "sacrebleu (>=2.0.0)", "huggingface-hub (<0.7)", "codecov (>=2.1)", "coverage (>5.2)", "pypesq", "jiwer (>=2.3.0)", "cloudpickle (>=1.3)", "bert-score (==0.3.10)", "torch-complex", "rouge-score (>=0.0.4)", "fast-bss-eval (>=0.1.0)", "scikit-image (>0.17.1)", "pytorch-msssim", "transformers (>=4.0)", "mypy (>=0.790)", "pytorch-lightning (>=1.5)", "scipy", "lpips", "torch-fidelity", "torchvision", "sphinxcontrib-mockautodoc", "pandoc (>=1.0)", "sphinx-togglebutton (>=0.2)", "myst-parser", "sphinx (>=4.0,<5.0)", "sphinx-autodoc-typehints (>=1.0)", "sphinx-paramlinks (>=0.5.1)", "sphinxcontrib-fulltoc (>=1.0)", "docutils (>=0.16)", "nbsphinx (>=0.8)", "sphinx-copybutton (>=0.3)", "pycocotools", "torchvision (>=0.8)", "pystoi"]
+detection = ["torchvision (>=0.8)", "pycocotools"]
+docs = ["sphinx-copybutton (>=0.3)", "nbsphinx (>=0.8)", "docutils (>=0.16)", "sphinxcontrib-fulltoc (>=1.0)", "sphinx-paramlinks (>=0.5.1)", "sphinx-autodoc-typehints (>=1.0)", "sphinx (>=4.0,<5.0)", "myst-parser", "sphinx-togglebutton (>=0.2)", "pandoc (>=1.0)", "sphinxcontrib-mockautodoc"]
+image = ["torchvision", "torch-fidelity", "lpips", "scipy"]
+integrate = ["pytorch-lightning (>=1.5)"]
+test = ["mypy (>=0.790)", "transformers (>=4.0)", "pytorch-msssim", "scikit-image (>0.17.1)", "fast-bss-eval (>=0.1.0)", "pycocotools", "rouge-score (>=0.0.4)", "torch-complex", "bert-score (==0.3.10)", "cloudpickle (>=1.3)", "jiwer (>=2.3.0)", "pypesq", "coverage (>5.2)", "codecov (>=2.1)", "huggingface-hub (<0.7)", "sacrebleu (>=2.0.0)", "pytest-cov (>2.10)", "psutil", "pre-commit (>=1.0)", "requests", "pytest (>=6.0.0,<7.0.0)", "twine (>=3.2)", "scikit-learn (>1.0,<1.1.1)", "pytest-doctestplus (>=0.9.0)", "pytest-timeout", "phmdoctest (>=1.1.1)", "check-manifest", "mir-eval (>=0.6)", "fire"]
+text = ["nltk (>=3.6)", "regex (>=2021.9.24)", "tqdm (>=4.41.0)"]
 
 [[package]]
 name = "tornado"
@@ -2851,10 +2851,10 @@ python-versions = ">=3.6"
 click = ">=7.1.1,<9.0.0"
 
 [package.extras]
-test = ["isort (>=5.0.6,<6.0.0)", "black (>=22.3.0,<23.0.0)", "mypy (==0.910)", "pytest-sugar (>=0.9.4,<0.10.0)", "pytest-xdist (>=1.32.0,<2.0.0)", "coverage (>=5.2,<6.0)", "pytest-cov (>=2.10.0,<3.0.0)", "pytest (>=4.4.0,<5.4.0)", "shellingham (>=1.3.0,<2.0.0)"]
-doc = ["mdx-include (>=1.4.1,<2.0.0)", "mkdocs-material (>=8.1.4,<9.0.0)", "mkdocs (>=1.1.2,<2.0.0)"]
-dev = ["pre-commit (>=2.17.0,<3.0.0)", "flake8 (>=3.8.3,<4.0.0)", "autoflake (>=1.3.1,<2.0.0)"]
-all = ["shellingham (>=1.3.0,<2.0.0)", "colorama (>=0.4.3,<0.5.0)"]
+all = ["colorama (>=0.4.3,<0.5.0)", "shellingham (>=1.3.0,<2.0.0)"]
+dev = ["autoflake (>=1.3.1,<2.0.0)", "flake8 (>=3.8.3,<4.0.0)", "pre-commit (>=2.17.0,<3.0.0)"]
+doc = ["mkdocs (>=1.1.2,<2.0.0)", "mkdocs-material (>=8.1.4,<9.0.0)", "mdx-include (>=1.4.1,<2.0.0)"]
+test = ["shellingham (>=1.3.0,<2.0.0)", "pytest (>=4.4.0,<5.4.0)", "pytest-cov (>=2.10.0,<3.0.0)", "coverage (>=5.2,<6.0)", "pytest-xdist (>=1.32.0,<2.0.0)", "pytest-sugar (>=0.9.4,<0.10.0)", "mypy (==0.910)", "black (>=22.3.0,<23.0.0)", "isort (>=5.0.6,<6.0.0)"]
 
 [[package]]
 name = "types-deprecated"
@@ -2982,7 +2982,7 @@ optional = false
 python-versions = ">=3.5"
 
 [package.extras]
-dev = ["black (>=19.3b0)", "pytest (>=4.6.2)"]
+dev = ["pytest (>=4.6.2)", "black (>=19.3b0)"]
 
 [[package]]
 name = "wrapt"
@@ -3035,7 +3035,10 @@ python-versions = ">=3.7.1, <3.10.0"
 content-hash = "6e059f258cfc27d3f7901c6d14e7ef7d2ff9acc25563c40cda1bf2601d38e7ac"
 
 [metadata.files]
-absl-py = []
+absl-py = [
+    {file = "absl-py-1.1.0.tar.gz", hash = "sha256:3aa39f898329c2156ff525dfa69ce709e42d77aab18bf4917719d6f260aa6a08"},
+    {file = "absl_py-1.1.0-py3-none-any.whl", hash = "sha256:db97287655e30336938f8058d2c81ed2be6af1d9b6ebbcd8df1080a6c7fcd24e"},
+]
 aiohttp = [
     {file = "aiohttp-3.8.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:1ed0b6477896559f17b9eaeb6d38e07f7f9ffe40b9f0f9627ae8b9926ae260a8"},
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@@ -3119,8 +3122,13 @@ alabaster = [
     {file = "alabaster-0.7.12.tar.gz", hash = "sha256:a661d72d58e6ea8a57f7a86e37d86716863ee5e92788398526d58b26a4e4dc02"},
 ]
 alembic = []
-antlr4-python3-runtime = []
-appnope = []
+antlr4-python3-runtime = [
+    {file = "antlr4-python3-runtime-4.9.3.tar.gz", hash = "sha256:f224469b4168294902bb1efa80a8bf7855f24c99aef99cbefc1bcd3cce77881b"},
+]
+appnope = [
+    {file = "appnope-0.1.3-py2.py3-none-any.whl", hash = "sha256:265a455292d0bd8a72453494fa24df5a11eb18373a60c7c0430889f22548605e"},
+    {file = "appnope-0.1.3.tar.gz", hash = "sha256:02bd91c4de869fbb1e1c50aafc4098827a7a54ab2f39d9dcba6c9547ed920e24"},
+]
 argon2-cffi = [
     {file = "argon2-cffi-21.3.0.tar.gz", hash = "sha256:d384164d944190a7dd7ef22c6aa3ff197da12962bd04b17f64d4e93d934dba5b"},
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@@ -3148,32 +3156,207 @@ argon2-cffi-bindings = [
     {file = "argon2_cffi_bindings-21.2.0-pp38-pypy38_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ed2937d286e2ad0cc79a7087d3c272832865f779430e0cc2b4f3718d3159b0cb"},
     {file = "argon2_cffi_bindings-21.2.0-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:5e00316dabdaea0b2dd82d141cc66889ced0cdcbfa599e8b471cf22c620c329a"},
 ]
-async-timeout = []
+async-timeout = [
+    {file = "async-timeout-4.0.2.tar.gz", hash = "sha256:2163e1640ddb52b7a8c80d0a67a08587e5d245cc9c553a74a847056bc2976b15"},
+    {file = "async_timeout-4.0.2-py3-none-any.whl", hash = "sha256:8ca1e4fcf50d07413d66d1a5e416e42cfdf5851c981d679a09851a6853383b3c"},
+]
 asynctest = [
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 ]
 atomicwrites = []
-attrs = []
-autopage = []
-babel = []
+attrs = [
+    {file = "attrs-21.4.0-py2.py3-none-any.whl", hash = "sha256:2d27e3784d7a565d36ab851fe94887c5eccd6a463168875832a1be79c82828b4"},
+    {file = "attrs-21.4.0.tar.gz", hash = "sha256:626ba8234211db98e869df76230a137c4c40a12d72445c45d5f5b716f076e2fd"},
+]
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+    {file = "Babel-2.10.3.tar.gz", hash = "sha256:7614553711ee97490f732126dc077f8d0ae084ebc6a96e23db1482afabdb2c51"},
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 backcall = [
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 ]
-beautifulsoup4 = []
-black = []
-bleach = []
+beautifulsoup4 = [
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+    {file = "beautifulsoup4-4.11.1.tar.gz", hash = "sha256:ad9aa55b65ef2808eb405f46cf74df7fcb7044d5cbc26487f96eb2ef2e436693"},
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+    {file = "ujson-5.4.0-pp37-pypy37_pp73-win_amd64.whl", hash = "sha256:9ae1d0094ce730e39e09656bc14074d9573cdd80adec1a55b06d8bf1f9613a01"},
+    {file = "ujson-5.4.0-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:784dbd12925845a3f0757a956447e2fd31418abb5aeaebf3aca1203195f16fd1"},
+    {file = "ujson-5.4.0-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:422653083c6df6cec17fdb5d6106c209aad9b0c94131c53b073980403db22167"},
+    {file = "ujson-5.4.0-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e91947fda8354ea7faf698b084ebcdbabd239e7b15d8436fb74394f59a207ac9"},
+    {file = "ujson-5.4.0-pp38-pypy38_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ef985eb2770900a485431910bd3f333b56d1a34b65f8c26a6ed8e8adf55f98d9"},
+    {file = "ujson-5.4.0-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:025758cf6561af6986d77cd4af9367ab56dde5c7c50f13f59e6964b4b25df73e"},
+    {file = "ujson-5.4.0.tar.gz", hash = "sha256:6b953e09441e307504130755e5bd6b15850178d591f66292bba4608c4f7f9b00"},
+]
 urllib3 = []
 wandb = []
 wcwidth = [
@@ -3924,9 +5257,18 @@ webencodings = [
     {file = "webencodings-0.5.1-py2.py3-none-any.whl", hash = "sha256:a0af1213f3c2226497a97e2b3aa01a7e4bee4f403f95be16fc9acd2947514a78"},
     {file = "webencodings-0.5.1.tar.gz", hash = "sha256:b36a1c245f2d304965eb4e0a82848379241dc04b865afcc4aab16748587e1923"},
 ]
-werkzeug = []
-widgetsnbextension = []
-win32-setctime = []
+werkzeug = [
+    {file = "Werkzeug-2.1.2-py3-none-any.whl", hash = "sha256:72a4b735692dd3135217911cbeaa1be5fa3f62bffb8745c5215420a03dc55255"},
+    {file = "Werkzeug-2.1.2.tar.gz", hash = "sha256:1ce08e8093ed67d638d63879fd1ba3735817f7a80de3674d293f5984f25fb6e6"},
+]
+widgetsnbextension = [
+    {file = "widgetsnbextension-3.6.1-py2.py3-none-any.whl", hash = "sha256:954e0faefdd414e4e013f17dbc7fd86f24cf1d243a3ac85d5f0fc2c2d2b50c66"},
+    {file = "widgetsnbextension-3.6.1.tar.gz", hash = "sha256:9c84ae64c2893c7cbe2eaafc7505221a795c27d68938454034ac487319a75b10"},
+]
+win32-setctime = [
+    {file = "win32_setctime-1.1.0-py3-none-any.whl", hash = "sha256:231db239e959c2fe7eb1d7dc129f11172354f98361c4fa2d6d2d7e278baa8aad"},
+    {file = "win32_setctime-1.1.0.tar.gz", hash = "sha256:15cf5750465118d6929ae4de4eb46e8edae9a5634350c01ba582df868e932cb2"},
+]
 wrapt = []
 yarl = [
     {file = "yarl-1.7.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:f2a8508f7350512434e41065684076f640ecce176d262a7d54f0da41d99c5a95"},

From 43b78e429dc9ed0fac231a05a777e44f52987cb6 Mon Sep 17 00:00:00 2001
From: "d.a.bunin" <d.a.bunin@tinkoff.ru>
Date: Fri, 5 Aug 2022 12:12:27 +0300
Subject: [PATCH 20/22] Remove unnecessary doctest

---
 etna/libs/pmdarima_utils/arima.py | 6 ------
 1 file changed, 6 deletions(-)

diff --git a/etna/libs/pmdarima_utils/arima.py b/etna/libs/pmdarima_utils/arima.py
index e05031d00..ae7bdb3be 100644
--- a/etna/libs/pmdarima_utils/arima.py
+++ b/etna/libs/pmdarima_utils/arima.py
@@ -80,12 +80,6 @@ def ARMAtoMA(ar, ma, max_deg):
         + \Sum_{j=1}^{min(i-1,p)}[ar_j*ema_{i-j}] + ma_i(but 0 if i>q) \\
         = \sum_{j=1}{min(i,p)}[ar_j*ema_{i-j}(but 1 if j=i)] \\
         + ma_i(but 0 if i>q)
-    Examples
-    --------
-    >>> ar = np.array([0.1])
-    >>> ma = np.empty(0)
-    >>> ARMAtoMA(ar, ma, 3)
-    array[0.1, 0.01, 0.001]
     """
     p = len(ar)
     q = len(ma)

From 9163dbd2799d7e549f46be628135529dcb330992 Mon Sep 17 00:00:00 2001
From: "d.a.bunin" <d.a.bunin@tinkoff.ru>
Date: Fri, 5 Aug 2022 18:16:37 +0300
Subject: [PATCH 21/22] Remove unnecessary test, add test on
 simple_differencing

---
 tests/test_models/test_sarimax_model.py | 80 +++++++------------------
 1 file changed, 22 insertions(+), 58 deletions(-)

diff --git a/tests/test_models/test_sarimax_model.py b/tests/test_models/test_sarimax_model.py
index f5a34a7c3..4a6e581c0 100644
--- a/tests/test_models/test_sarimax_model.py
+++ b/tests/test_models/test_sarimax_model.py
@@ -1,29 +1,27 @@
-import numpy as np
 import pytest
 from statsmodels.tsa.statespace.sarimax import SARIMAX
 
-from etna.datasets import TSDataset
-from etna.datasets import generate_ar_df
 from etna.models import SARIMAXModel
 from etna.pipeline import Pipeline
 
 
+def _check_prediction(ts, model, horizon):
+    model.fit(ts)
+    future_ts = ts.make_future(future_steps=horizon)
+    res = model.forecast(future_ts)
+    res = res.to_pandas(flatten=True)
+
+    assert not res.isnull().values.any()
+    assert len(res) == 14
+
+
 def test_sarimax_forecaster_run(example_tsds):
     """
     Given: I have dataframe with 2 segments
     When:
     Then: I get 7 periods per dataset as a forecast
     """
-
-    horizon = 7
-    model = SARIMAXModel()
-    model.fit(example_tsds)
-    future_ts = example_tsds.make_future(future_steps=horizon)
-    res = model.forecast(future_ts)
-    res = res.to_pandas(flatten=True)
-
-    assert not res.isnull().values.any()
-    assert len(res) == 14
+    _check_prediction(ts=example_tsds, model=SARIMAXModel(), horizon=7)
 
 
 def test_sarimax_save_regressors_on_fit(example_reg_tsds):
@@ -43,21 +41,22 @@ def test_sarimax_select_regressors_correctly(example_reg_tsds):
         assert (segment_regressors == segment_regressors_expected).all().all()
 
 
-def test_sarimax_forecaster_run_with_reg(example_reg_tsds):
+def test_sarimax_forecaster_run_with_simple_differencing(example_tsds):
     """
     Given: I have dataframe with 2 segments
     When:
     Then: I get 7 periods per dataset as a forecast
     """
-    horizon = 7
-    model = SARIMAXModel()
-    model.fit(example_reg_tsds)
-    future_ts = example_reg_tsds.make_future(future_steps=horizon)
-    res = model.forecast(future_ts)
-    res = res.to_pandas(flatten=True)
+    _check_prediction(ts=example_tsds, model=SARIMAXModel(simple_differencing=True), horizon=7)
 
-    assert not res.isnull().values.any()
-    assert len(res) == 14
+
+def test_sarimax_forecaster_run_with_reg(example_reg_tsds):
+    """
+    Given: I have dataframe with 2 segments
+    When:
+    Then: I get 7 periods per dataset as a forecast
+    """
+    _check_prediction(ts=example_reg_tsds, model=SARIMAXModel(), horizon=7)
 
 
 def test_sarimax_forececaster_run_with_reg_custom_order(example_reg_tsds):
@@ -66,15 +65,7 @@ def test_sarimax_forececaster_run_with_reg_custom_order(example_reg_tsds):
     When: Sarimax have non standard `order` param
     Then: I get 7 periods per dataset as a forecast
     """
-    horizon = 7
-    model = SARIMAXModel(order=(3, 1, 0))
-    model.fit(example_reg_tsds)
-    future_ts = example_reg_tsds.make_future(future_steps=horizon)
-    res = model.forecast(future_ts)
-    res = res.to_pandas(flatten=True)
-
-    assert not res.isnull().values.any()
-    assert len(res) == 14
+    _check_prediction(ts=example_reg_tsds, model=SARIMAXModel(order=(3, 1, 0)), horizon=7)
 
 
 def test_prediction_interval_run_insample(example_tsds):
@@ -137,30 +128,3 @@ def test_sarimax_forecast_1_point(example_tsds):
     assert len(pred.df) == horizon
     pred_quantiles = model.forecast(future_ts, prediction_interval=True, quantiles=[0.025, 0.8])
     assert len(pred_quantiles.df) == horizon
-
-
-def test_prediction_simple_differencing():
-    """Check that SARIMAX gives similar results with different values of ``simple_differencing``.
-
-    We generate dataset from ``generate_ar_df`` with ``ar_coef=[1]`` and it gives us (0, 1, 1) process.
-    """
-    horizon = 7
-    df = generate_ar_df(periods=100, n_segments=3, start_time="2020-01-01", ar_coef=[1])
-    ts = TSDataset(df=TSDataset.to_dataset(df), freq="D")
-
-    # prepare prediction from regular model
-    model_regular = SARIMAXModel(order=(0, 1, 1))
-    model_regular.fit(ts)
-    future_ts = ts.make_future(future_steps=horizon)
-    regular_prediction = model_regular.forecast(future_ts)
-    regular_prediction = regular_prediction.to_pandas(flatten=True)
-
-    # prepare prediction from model with simple differencing
-    model_simplified = SARIMAXModel(order=(0, 1, 1), simple_differencing=True)
-    model_simplified.fit(ts)
-    future_ts = ts.make_future(future_steps=horizon)
-    simplified_prediction = model_simplified.forecast(future_ts)
-    simplified_prediction = simplified_prediction.to_pandas(flatten=True)
-
-    correlation = np.corrcoef(regular_prediction["target"], simplified_prediction["target"])[0, 1]
-    assert correlation >= 0.95

From 1b664c118a70266d0a5602f6ec72398dfdca2d39 Mon Sep 17 00:00:00 2001
From: "d.a.bunin" <d.a.bunin@tinkoff.ru>
Date: Fri, 5 Aug 2022 19:17:30 +0300
Subject: [PATCH 22/22] Add more comments about usage of determine_num_steps

---
 etna/models/sarimax.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/etna/models/sarimax.py b/etna/models/sarimax.py
index 46cc30ce7..de1d19b8f 100644
--- a/etna/models/sarimax.py
+++ b/etna/models/sarimax.py
@@ -270,12 +270,15 @@ def predict(self, df: pd.DataFrame, prediction_interval: bool, quantiles: Sequen
         exog_future = self._select_regressors(df)
         start_timestamp = df["timestamp"].min()
         end_timestamp = df["timestamp"].max()
+        # determine index of start_timestamp if counting from first timestamp of train
         start_idx = determine_num_steps(
             start_timestamp=self._first_train_timestamp, end_timestamp=start_timestamp, freq=self._freq  # type: ignore
         )
+        # determine index of end_timestamp if counting from first timestamp of train
         end_idx = determine_num_steps(
             start_timestamp=self._first_train_timestamp, end_timestamp=end_timestamp, freq=self._freq  # type: ignore
         )
+
         if prediction_interval:
             forecast, _ = seasonal_prediction_with_confidence(
                 arima_res=self._result, start=start_idx, end=end_idx, X=exog_future, alpha=0.05