From 3223f4bff25ca80662f35a11c196c2a2f878e7c8 Mon Sep 17 00:00:00 2001 From: Colin Catlin Date: Mon, 6 May 2024 19:57:33 -0500 Subject: [PATCH 1/9] 0.6.13 a1 --- README.md | 2 +- TODO.md | 10 ++-------- autots/models/matrix_var.py | 4 ++-- autots/models/prophet.py | 31 +++++++++++++++++++++++++++---- 4 files changed, 32 insertions(+), 15 deletions(-) diff --git a/README.md b/README.md index 3ba2b750..0469bafb 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,7 @@ AutoTS is a time series package for Python designed for rapidly deploying high-accuracy forecasts at scale. -In 2023, AutoTS has won in the M6 forecasting competition, delivering the highest performance investment decisions across 12 months of stock market forecasting. +In 2023, AutoTS won in the M6 forecasting competition, delivering the highest performance investment decisions across 12 months of stock market forecasting. There are dozens of forecasting models usable in the `sklearn` style of `.fit()` and `.predict()`. These includes naive, statistical, machine learning, and deep learning models. diff --git a/TODO.md b/TODO.md index 45ece226..a21b2a86 100644 --- a/TODO.md +++ b/TODO.md @@ -13,14 +13,8 @@ * Forecasts are desired for the future immediately following the most recent data. * trimmed_mean to AverageValueNaive -# 0.6.12 🇺🇦 🇺🇦 🇺🇦 -* bug fixes -* added DMD model -* modified the `constraints` options so it now accepts of list of dictionaries of constraints with new last_window and slope options -* 'dampening' as a constraint method to dampen all forecasts, fixed Cassandra trend_phi dampening -* new med_diff anomaly method and 'laplace' added as distribution option -* modified fourier_df to now work with sub daily data -* some madness with wavelets attempting to use them like fourier series for seasonality +# 0.6.13 🇺🇦 🇺🇦 🇺🇦 +* trend_phi directly into Prophet ### Unstable Upstream Pacakges (those that are frequently broken by maintainers) * Pytorch-Forecasting diff --git a/autots/models/matrix_var.py b/autots/models/matrix_var.py index ab611861..19df5904 100644 --- a/autots/models/matrix_var.py +++ b/autots/models/matrix_var.py @@ -236,8 +236,8 @@ def predict( def get_new_params(self, method: str = 'random'): """Return dict of new parameters for parameter tuning.""" return { - 'method': random.choice(['als', 'dmd']), - 'rank': random.choice([2, 4, 0.1, 0.2, 0.5]), + 'method': random.choices(['als', 'dmd'], [0.7, 0.3])[0], + 'rank': random.choice([2, 4, 6, 0.1, 0.2, 0.5]), 'maxiter': 200, } diff --git a/autots/models/prophet.py b/autots/models/prophet.py index ecb6dc8a..e1622cf7 100644 --- a/autots/models/prophet.py +++ b/autots/models/prophet.py @@ -57,6 +57,7 @@ def __init__( changepoint_range: float = 0.8, seasonality_prior_scale: float = 10.0, holidays_prior_scale: float = 10.0, + trend_phi: float = 1, random_seed: int = 2020, verbose: int = 0, n_jobs: int = None, @@ -84,6 +85,7 @@ def __init__( self.changepoint_range = changepoint_range self.seasonality_prior_scale = seasonality_prior_scale self.holidays_prior_scale = holidays_prior_scale + self.trend_phi = trend_phi def fit(self, df, future_regressor=None): """Train algorithm given data supplied. @@ -219,12 +221,31 @@ def seek_the_oracle( a = np.append(args['regressor_train'], future_regressor.values) future[args['regressor_name']] = a fcst = m.predict(future) - fcst = fcst.tail(forecast_length) # remove the backcast - forecast = fcst['yhat'] + # fcst = fcst.tail(forecast_length) # remove the backcast + if self.trend_phi is not None and self.trend_phi != 1 and forecast_length > 2: + req_len = fcst.shape[0] - 1 + phi_series = pd.Series( + [self.trend_phi] * req_len, + index=fcst.index[1:], + ).pow(range(req_len)) + + # adjust all by same margin + fcst['trend'] = pd.concat( + [ + fcst['trend'].iloc[0:1], + fcst['trend'].diff().iloc[1:].mul(phi_series, axis=0), + ] + ).cumsum() + # for now, not doing the dampening on upper and lower bounds as those might still be good to have the full probability + forecast = fcst['trend'] * (1 + fcst["multiplicative_terms"]) + fcst['additive_terms'] + upper_forecast = fcst['trend_upper'] * (1 + fcst["multiplicative_terms_upper"]) + fcst['additive_terms_upper'] + lower_forecast = fcst['trend_lower'] * (1 + fcst["multiplicative_terms_lower"]) + fcst['additive_terms_lower'] + else: + forecast = fcst['yhat'] + lower_forecast = fcst['yhat_lower'] + upper_forecast = fcst['yhat_upper'] forecast.name = series - lower_forecast = fcst['yhat_lower'] lower_forecast.name = series - upper_forecast = fcst['yhat_upper'] upper_forecast.name = series return (forecast, lower_forecast, upper_forecast) @@ -345,6 +366,7 @@ def get_new_params(self, method: str = 'random'): 'n_changepoints': random.choices( [5, 10, 20, 25, 30, 40, 50], [0.05, 0.1, 0.1, 0.9, 0.1, 0.05, 0.05] )[0], + "trend_phi": random.choices([None, 0.98, 0.999, 0.95, 0.8], [0.8, 0.1, 0.2, 0.1, 0.1])[0], } def get_params(self): @@ -359,6 +381,7 @@ def get_params(self): "changepoint_range": self.changepoint_range, "seasonality_prior_scale": self.seasonality_prior_scale, "holidays_prior_scale": self.holidays_prior_scale, + "trend_phi": self.trend_phi, } From 466a0c3a3a8e956deb0995cf90dfd6568a8728ee Mon Sep 17 00:00:00 2001 From: Colin Catlin Date: Mon, 6 May 2024 20:56:16 -0500 Subject: [PATCH 2/9] 0.6.13 a2 --- autots/tools/seasonal.py | 4 ++-- autots/tools/transform.py | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/autots/tools/seasonal.py b/autots/tools/seasonal.py index b0fbfc35..d9a2f724 100644 --- a/autots/tools/seasonal.py +++ b/autots/tools/seasonal.py @@ -610,8 +610,8 @@ def random_datepart(method='random'): if predefined: seasonalities = [random.choice(date_part_methods)] else: - comp_opts = datepart_components + [7, 365.25, 12, 52, 168] - seasonalities = random.choices(comp_opts, k=2) + comp_opts = datepart_components + [7, 365.25, 12, 52, 168, 24] + seasonalities = random.choices(comp_opts, k=3) return seasonalities diff --git a/autots/tools/transform.py b/autots/tools/transform.py index ed3e6ae3..edd0dbdf 100644 --- a/autots/tools/transform.py +++ b/autots/tools/transform.py @@ -4094,8 +4094,8 @@ def get_new_params(method: str = "random"): """Generate new random parameters""" return { "cutoff": random.choices( - [0.005, 0.01, 0.05, 0.1, 0.2, 0.4, 0.8], - [0.1, 0.2, 0.1, 0.2, 0.2, 0.2, 0.1], + [0.005, 0.01, 0.05, 0.1, 0.2, 0.4, 0.8, 7, 365], + [0.1, 0.2, 0.1, 0.2, 0.2, 0.2, 0.1, 0.1, 0.1], )[0], "reverse": random.choices([False, True], [0.9, 0.1])[0], } From 2b0c0143c5ba1dc5a630b46b803b40cf37e3c23e Mon Sep 17 00:00:00 2001 From: Colin Catlin Date: Wed, 8 May 2024 18:11:31 -0500 Subject: [PATCH 3/9] 0.6.13 a3 --- autots/models/sklearn.py | 15 ++------------- autots/tools/seasonal.py | 5 ++++- autots/tools/transform.py | 15 +-------------- 3 files changed, 7 insertions(+), 28 deletions(-) diff --git a/autots/models/sklearn.py b/autots/models/sklearn.py index 84954c55..9a3f6ebd 100644 --- a/autots/models/sklearn.py +++ b/autots/models/sklearn.py @@ -18,7 +18,7 @@ pass from autots.models.base import ModelObject, PredictionObject from autots.tools.probabilistic import Point_to_Probability -from autots.tools.seasonal import date_part, seasonal_int +from autots.tools.seasonal import date_part, seasonal_int, random_datepart from autots.tools.window_functions import window_maker, last_window, sliding_window_view from autots.tools.cointegration import coint_johansen, btcd_decompose from autots.tools.holiday import holiday_flag @@ -2429,18 +2429,7 @@ def get_new_params(self, method: str = 'random'): ) else: model_choice = generate_regressor_params(model_dict=datepart_model_dict) - datepart_choice = random.choices( - [ - "recurring", - "simple", - "expanded", - "simple_2", - "simple_binarized", - "expanded_binarized", - 'common_fourier', - ], - [0.4, 0.3, 0.3, 0.3, 0.4, 0.05, 0.05], - )[0] + datepart_choice = random_datepart() if datepart_choice in ["simple", "simple_2", "recurring", "simple_binarized"]: polynomial_choice = random.choices([None, 2, 3], [0.5, 0.2, 0.01])[0] else: diff --git a/autots/tools/seasonal.py b/autots/tools/seasonal.py index d9a2f724..288b5bb3 100644 --- a/autots/tools/seasonal.py +++ b/autots/tools/seasonal.py @@ -577,10 +577,11 @@ def create_seasonality_feature(DTindex, t, seasonality, history_days=None): [168, "hour"], ["morlet_365.25_12_12", "ricker_7_7_1"], ["db2_365.25_12_0.5", "morlet_7_7_1"], + ["weekdaymonthofyear", "quarter", "dayofweek"], + "lunar_phase", "other", ] - def random_datepart(method='random'): """New random parameters for seasonality.""" seasonalities = random.choices( @@ -602,6 +603,8 @@ def random_datepart(method='random'): 0.1, 0.1, 0.1, + 0.05, + 0.05, 0.3, ], )[0] diff --git a/autots/tools/transform.py b/autots/tools/transform.py index edd0dbdf..a9d329bd 100644 --- a/autots/tools/transform.py +++ b/autots/tools/transform.py @@ -1162,20 +1162,7 @@ def __init__( @staticmethod def get_new_params(method: str = "random", holiday_countries_used=None): - datepart_choice = random.choices( - [ - "simple", - "expanded", - "recurring", - "simple_2", - "simple_binarized", - "lunar_phase", - "common_fourier", - ["dayofweek", 365.25], - ["weekdaymonthofyear", "quarter", "dayofweek"], - ], - [0.1, 0.25, 0.2, 0.1, 0.3, 0.001, 0.1, 0.1, 0.03], - )[0] + datepart_choice = random_datepart() if datepart_choice in ["simple", "simple_2", "recurring"]: polynomial_choice = random.choices([None, 2], [0.5, 0.2])[0] else: From 0c8d6655ecd05cc9afebfa5caae01608d3a31c76 Mon Sep 17 00:00:00 2001 From: Colin Catlin Date: Thu, 9 May 2024 08:23:58 -0500 Subject: [PATCH 4/9] 0.6.13 a4 --- autots/tools/seasonal.py | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/autots/tools/seasonal.py b/autots/tools/seasonal.py index 288b5bb3..d73dc358 100644 --- a/autots/tools/seasonal.py +++ b/autots/tools/seasonal.py @@ -437,6 +437,9 @@ def fourier_df(DTindex, seasonality, order=10, t=None, history_days=None): "is_quarter_start", "is_quarter_end", "days_from_epoch", + "isoweek", + "isoweek_binary", + "isoday", ] @@ -535,6 +538,17 @@ def create_datepart_components(DTindex, seasonality): return pd.DataFrame({'is_quarter_end': DTindex.is_quarter_end}) elif seasonality == "days_from_epoch": return (DTindex - pd.Timestamp('2000-01-01')).days.astype('int32') + elif seasonality == "isoweek": + return DTindex.isocalendar().week + elif seasonality == "isoweek_binary": + return pd.get_dummies( + pd.Categorical( + DTindex.isocalendar().week, categories=list(range(1, 54)), ordered=True + ), + dtype=np.uint16, + ).rename(columns=lambda x: f"{seasonality}_" + str(x)) + elif seasonality == "isoday": + return DTindex.isocalendar().day else: raise ValueError( f"create_datepart_components `{seasonality}` is not recognized" From b09f46c4e50282a9d72b0044dbce35d06a3b0231 Mon Sep 17 00:00:00 2001 From: Colin Catlin Date: Sat, 11 May 2024 00:04:49 -0500 Subject: [PATCH 5/9] 0.6.13 a5 --- TODO.md | 1 + autots/models/basics.py | 88 +++++++++++++++++++++++++++++-------- autots/tools/fast_kalman.py | 50 ++++++++++++++++++++- 3 files changed, 118 insertions(+), 21 deletions(-) diff --git a/TODO.md b/TODO.md index a21b2a86..524c913b 100644 --- a/TODO.md +++ b/TODO.md @@ -15,6 +15,7 @@ # 0.6.13 🇺🇦 🇺🇦 🇺🇦 * trend_phi directly into Prophet +* subset arg to make KalmanStateSpace more scalable to memory ### Unstable Upstream Pacakges (those that are frequently broken by maintainers) * Pytorch-Forecasting diff --git a/autots/models/basics.py b/autots/models/basics.py index 3de3ce03..025d0b22 100644 --- a/autots/models/basics.py +++ b/autots/models/basics.py @@ -2057,7 +2057,7 @@ class KalmanStateSpace(ModelObject): name (str): String to identify class frequency (str): String alias of datetime index frequency or else 'infer' prediction_interval (float): Confidence interval for probabilistic forecast - + subset (int): if not None, forecasts in chunks of this size. Reduces memory at the expense of compute time. """ def __init__( @@ -2075,6 +2075,7 @@ def __init__( em_iter: int = 10, model_name: str = "undefined", forecast_length: int = None, + subset=None, **kwargs, ): ModelObject.__init__( @@ -2093,6 +2094,7 @@ def __init__( self.em_iter = em_iter self.model_name = model_name self.forecast_length = forecast_length + self.subset = subset def fit(self, df, future_regressor=None): """Train algorithm given data supplied. @@ -2102,31 +2104,55 @@ def fit(self, df, future_regressor=None): """ self.fit_data(df) + if self.subset is None: + self.kf = self._fit(df, future_regressor=None) + elif isinstance(self.subset, (float, int)): + if self.subset < 1 and self.subset > 0: + self.subset = self.subset * df.shape[1] + chunks = df.shape[1] // self.subset + if chunks > 1: + self.kf = {} + self.subset_columns = {} + if (df.shape[1] % self.subset) != 0: + chunks += 1 + for x in range(chunks): + subset = df.iloc[:, self.subset * x: (self.subset * (x + 1))] + self.subset_columns[str(x)] = subset.columns.tolist() + self.kf[str(x)] = self._fit(subset, future_regressor=None) + else: + self.kf = self._fit(df, future_regressor=None) + else: + raise ValueError(f"subset arg {self.subset} not recognized") + + self.fit_runtime = datetime.datetime.now() - self.startTime + return self + + def _fit(self, df, future_regressor=None): if self.observation_noise == "auto": self.fit_noise = self.tune_observational_noise(df)[0] else: self.fit_noise = self.observation_noise - self.kf = KalmanFilter( + kf = KalmanFilter( state_transition=self.state_transition, # matrix A process_noise=self.process_noise, # Q observation_model=self.observation_model, # H observation_noise=self.fit_noise, # R ) if self.em_iter is not None: - self.kf = self.kf.em(self.df_train, n_iter=self.em_iter) + kf = kf.em(df.to_numpy().T, n_iter=self.em_iter) - self.fit_runtime = datetime.datetime.now() - self.startTime - return self + return kf def fit_data(self, df, future_regressor=None): df = self.basic_profile(df) - self.df_train = df.to_numpy().T + self.df_train = df # df.to_numpy().T self.train_index = df.index return self def cost_function(self, param, df): try: # evaluating on a single, most recent holdout only, for simplicity + local = df.to_numpy().T kf = KalmanFilter( state_transition=self.state_transition, # matrix A process_noise=self.process_noise, # Q @@ -2136,15 +2162,15 @@ def cost_function(self, param, df): ) if self.em_iter is not None: kf = kf.em( - self.df_train[:, : -self.forecast_length], n_iter=self.em_iter + local[:, : -self.forecast_length], n_iter=self.em_iter ) result = kf.predict( - self.df_train[:, : -self.forecast_length], self.forecast_length + local[:, : -self.forecast_length], self.forecast_length ) df_smooth = pd.DataFrame( result.observations.mean.T, index=df.index[-self.forecast_length :], - columns=self.column_names, + columns=df.columns, ) df_stdev = np.sqrt(result.observations.cov).T bound = df_stdev * norm.ppf(self.prediction_interval) @@ -2219,20 +2245,41 @@ def predict( "must provide forecast_length to KalmanStateSpace predict" ) predictStartTime = datetime.datetime.now() - result = self.kf.predict(self.df_train, forecast_length) - df = pd.DataFrame( - result.observations.mean.T, - index=self.create_forecast_index(forecast_length), - columns=self.column_names, - ) - - if just_point_forecast: - return df + future_index = self.create_forecast_index(forecast_length) + if isinstance(self.kf, dict): + forecasts = [] + uppers = [] + lowers = [] + for x in self.subset_columns: + current_cols = self.subset_columns[x] + result = self.kf[x].predict(self.df_train.reindex(columns=current_cols).to_numpy().T, forecast_length) + df = pd.DataFrame( + result.observations.mean.T, + index=future_index, + columns=current_cols, + ) + forecasts.append(df) + df_stdev = np.sqrt(result.observations.cov).T + bound = df_stdev * norm.ppf(self.prediction_interval) + uppers.append(df + bound) + lowers.append(df - bound) + df = pd.concat(forecasts, axis=1).reindex(columns=self.column_names) + upper_forecast = pd.concat(uppers, axis=1).reindex(columns=self.column_names) + lower_forecast = pd.concat(lowers, axis=1).reindex(columns=self.column_names) else: + result = self.kf.predict(self.df_train.to_numpy().T, forecast_length) + df = pd.DataFrame( + result.observations.mean.T, + index=future_index, + columns=self.column_names, + ) df_stdev = np.sqrt(result.observations.cov).T bound = df_stdev * norm.ppf(self.prediction_interval) upper_forecast = df + bound lower_forecast = df - bound + if just_point_forecast: + return df + else: predict_runtime = datetime.datetime.now() - predictStartTime prediction = PredictionObject( model_name=self.name, @@ -2251,7 +2298,9 @@ def predict( def get_new_params(self, method: str = "random"): # predefined, or random - return new_kalman_params(method=method) + new_params = new_kalman_params(method=method) + new_params['subset'] = 200 + return new_params def get_params(self): """Return dict of current parameters.""" @@ -2261,6 +2310,7 @@ def get_params(self): "process_noise": self.process_noise, "observation_model": self.observation_model, "observation_noise": self.observation_noise, + "subset": self.subset, } diff --git a/autots/tools/fast_kalman.py b/autots/tools/fast_kalman.py index 7a1ae1a5..f3615888 100644 --- a/autots/tools/fast_kalman.py +++ b/autots/tools/fast_kalman.py @@ -104,7 +104,7 @@ from functools import wraps -def random_state_space(): +def random_state_space_original(): """Return randomly generated statespace models.""" n_dims = random.choices([1, 2, 3, 4, 8], [0.1, 0.2, 0.3, 0.4, 0.3])[0] if n_dims == 1: @@ -135,6 +135,32 @@ def random_state_space(): return st, procnois, obsmod, obsnois +def ensure_stability(st): + eigenvalues, eigenvectors = np.linalg.eig(st) + # Scale eigenvalues to ensure their absolute values are less than 1 + stable_eigenvalues = eigenvalues / (np.abs(eigenvalues) + 1e-5) + st_stable = eigenvectors @ np.diag(stable_eigenvalues) @ np.linalg.inv(eigenvectors) + return st_stable.real + +def random_matrix(rows, cols, density=0.2): + matrix = np.random.randn(rows, cols) + sparsity_mask = np.random.rand(rows, cols) < density + return np.where(sparsity_mask, matrix, 0) + +def random_state_space(tries=10): + for _ in range(tries): + n_dims = random.choices([1, 2, 3, 4, 8], [0.1, 0.2, 0.3, 0.4, 0.3])[0] + st = random_matrix(n_dims, n_dims, density=0.5) + st = ensure_stability(st) + obsmod = random_matrix(1, n_dims, density=1.0) # Full observation for simplicity + procnois = np.diag(np.random.exponential(0.01, size=n_dims)).round(3) + obsnois = np.random.exponential(1.0) + + if np.all(np.abs(np.linalg.eigvals(st)) < 1): # Check stability + return st, procnois, obsmod, obsnois + raise ValueError("Failed to generate a stable model after several tries") + + def holt_winters_damped_matrices(M, alpha, beta, gamma, phi=1.0): """Not sure if this is correct. It's close, at least.""" # State Transition Matrix F @@ -457,6 +483,7 @@ def new_kalman_params(method=None, allow_auto=True): 'observation_noise': random.choice([0.01, 0.1, 1, 'auto']), }, "dynamic_linear", + "random_original", ], [ 0.1, @@ -476,6 +503,7 @@ def new_kalman_params(method=None, allow_auto=True): 0.1, 0.1, 0.1, + 0.1, ], )[0] if params in [364] and method not in ['deep']: @@ -489,6 +517,15 @@ def new_kalman_params(method=None, allow_auto=True): 'observation_model': obsmod.tolist(), 'observation_noise': obsnois, } + elif params == "random_original": + st, procnois, obsmod, obsnois = random_state_space_original() + params = { + 'model_name': 'randomly generated_original', + 'state_transition': st.tolist(), + 'process_noise': procnois.tolist(), + 'observation_model': obsmod.tolist(), + 'observation_noise': obsnois, + } elif params == "dynamic_linear": choices = [ 0.0, @@ -1313,6 +1350,11 @@ def douter(a, b): "Outer product, last two axes" return a * b.transpose((0, 2, 1)) +def stable_pinv(A, tol=1e-5, regularization=1e-4): + n = A.shape[1] + U, s, Vt = np.linalg.svd((A + regularization * np.eye(n)), full_matrices=False) + s_inv = np.where(s > tol, 1/s, 0) + return Vt.T @ np.diag(s_inv) @ U.T def dinv(A): "Matrix inverse applied to last two axes" @@ -1321,7 +1363,11 @@ def dinv(A): np.nan_to_num(A) ) # can cause kernel death in OpenBLAS with NaN except Exception: - res = np.linalg.pinv(A) # slower but more robust + try: + res = np.linalg.pinv(A) # slower but more robust + except np.linalg.LinAlgError: + print("SVD did not converge, attempting more robust approach...") + res = stable_pinv(A) return res From 7dca79ed268d530b81b83c10581ad043103db646 Mon Sep 17 00:00:00 2001 From: Colin Catlin Date: Sat, 11 May 2024 09:16:35 -0500 Subject: [PATCH 6/9] 0.6.13 a6 --- autots/evaluator/auto_ts.py | 2 +- autots/models/basics.py | 5 ++++- 2 files changed, 5 insertions(+), 2 deletions(-) diff --git a/autots/evaluator/auto_ts.py b/autots/evaluator/auto_ts.py index 2365ba23..6836c1a1 100644 --- a/autots/evaluator/auto_ts.py +++ b/autots/evaluator/auto_ts.py @@ -2195,7 +2195,7 @@ def export_template( max_per_model_class: int = None, include_results: bool = False, unpack_ensembles: bool = False, - min_metrics: list = ['smape', 'spl'], + min_metrics: list = ['smape', 'spl', 'wasserstein', 'mle', 'imle', 'ewmae'], max_metrics: list = None, ): """Export top results as a reusable template. diff --git a/autots/models/basics.py b/autots/models/basics.py index 025d0b22..5e98b6a6 100644 --- a/autots/models/basics.py +++ b/autots/models/basics.py @@ -2299,7 +2299,10 @@ def predict( def get_new_params(self, method: str = "random"): # predefined, or random new_params = new_kalman_params(method=method) - new_params['subset'] = 200 + if method in ['deep']: + new_params['subset'] = random.choices([None, 200, 300], [0.3, 0.3, 0.3])[0] + else: + new_params['subset'] = random.choices([100, 200, 300], [0.3, 0.3, 0.3])[0] # probably no difference return new_params def get_params(self): From 08c1d9a80cd5023b5f0b242ffaa42768c67804eb Mon Sep 17 00:00:00 2001 From: Colin Catlin Date: Mon, 13 May 2024 14:50:43 -0500 Subject: [PATCH 7/9] 0.6.13 a7 --- autots/templates/general.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/autots/templates/general.py b/autots/templates/general.py index 9f715fc2..d7a7313c 100644 --- a/autots/templates/general.py +++ b/autots/templates/general.py @@ -463,14 +463,14 @@ }, "74": { # optimized 200 minutes on initial model import on load_daily "Model": "DMD", - 'ModelParameters': '{"rank": 10, "alpha": 1, "amplitude_threshold": null, "eigenvalue_threshold": null}"', - "TransformationParameters": '"{"fillna": "linear", "transformations": {"0": "HistoricValues", "1": "AnomalyRemoval", "2": "SeasonalDifference", "3": "AnomalyRemoval"},"transformation_params": {"0": {"window": 10}, "1": {"method": "zscore", "method_params": {"distribution": "norm", "alpha": 0.05}, "fillna": "ffill", "transform_dict": {"fillna": null, "transformations": {"0": "ClipOutliers"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 6}}}, "isolated_only": false}, "2": {"lag_1": 7, "method": "Mean"}, "3": {"method": "zscore", "method_params": {"distribution": "norm", "alpha": 0.05}, "fillna": "fake_date", "transform_dict": {"transformations": {"0": "DifferencedTransformer"}, "transformation_params": {"0": {}}}, "isolated_only": false}}}', + 'ModelParameters': '{"rank": 10, "alpha": 1, "amplitude_threshold": null, "eigenvalue_threshold": null}', + "TransformationParameters": '''{"fillna": "linear", "transformations": {"0": "HistoricValues", "1": "AnomalyRemoval", "2": "SeasonalDifference", "3": "AnomalyRemoval"},"transformation_params": {"0": {"window": 10}, "1": {"method": "zscore", "method_params": {"distribution": "norm", "alpha": 0.05}, "fillna": "ffill", "transform_dict": {"fillna": null, "transformations": {"0": "ClipOutliers"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 6}}}, "isolated_only": false}, "2": {"lag_1": 7, "method": "Mean"}, "3": {"method": "zscore", "method_params": {"distribution": "norm", "alpha": 0.05}, "fillna": "fake_date", "transform_dict": {"transformations": {"0": "DifferencedTransformer"}, "transformation_params": {"0": {}}}, "isolated_only": false}}}''', "Ensemble": 0, }, "75": { # short optimization on M5 "Model": "DMD", - "ModelParameters": "{'rank': 2, 'alpha': 1, 'amplitude_threshold': null, 'eigenvalue_threshold': 1}", - "TransformationParameters": "{'fillna': 'ffill', 'transformations': {'0': 'SeasonalDifference', '1': 'AlignLastValue', '2': 'Round', '3': 'Round', '4': 'MinMaxScaler'}, 'transformation_params': {'0': {'lag_1': 7, 'method': 'LastValue'}, '1': {'rows': 1, 'lag': 1, 'method': 'additive', 'strength': 1.0, 'first_value_only': false}, '2': {'decimals': 0, 'on_transform': false, 'on_inverse': true}, '3': {'decimals': 0, 'on_transform': false, 'on_inverse': true}, '4': {}}}", + "ModelParameters": '''{"rank": 2, "alpha": 1, "amplitude_threshold": null, "eigenvalue_threshold": 1}''', + "TransformationParameters": '''{"fillna": "ffill", "transformations": {"0": "SeasonalDifference", "1": "AlignLastValue", "2": "Round", "3": "Round", "4": "MinMaxScaler"}, "transformation_params": {"0": {"lag_1": 7, "method": "LastValue"}, "1": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}, "2": {"decimals": 0, "on_transform": false, "on_inverse": true}, "3": {"decimals": 0, "on_transform": false, "on_inverse": true}, "4": {}}}''', "Ensemble": 0, }, } From 3a7e1942a83e06d47b40067d0863b9be5266f2c1 Mon Sep 17 00:00:00 2001 From: Colin Catlin Date: Mon, 13 May 2024 15:06:01 -0500 Subject: [PATCH 8/9] 0.6.13 a8 --- tests/test_autots.py | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/tests/test_autots.py b/tests/test_autots.py index 1f2d2212..803f57b0 100644 --- a/tests/test_autots.py +++ b/tests/test_autots.py @@ -17,6 +17,7 @@ from autots.models.model_list import default as default_model_list from autots.models.model_list import all_models from autots.evaluator.benchmark import Benchmark +from autots.templates.general import general_template from autots.tools.cpu_count import cpu_count, set_n_jobs @@ -546,6 +547,17 @@ def test_benchmark(self): print(bench.results) time.sleep(5) + def test_template(self): + for index, row in general_template.iterrows(): + model = row["Model"] + with self.subTest(i=model): + mod = json.loads(row['ModelParameters']) + trans = json.loads(row['TransformationParameters']) + ensemble = row["Ensemble"] + self.assertIsInstance(mod, dict) + self.assertIsInstance(trans, dict) + self.assertIsNotNone(ensemble) + # test all same on univariate input, non-horizontal, with regressor, and different frequency, with forecast_length = 1 ! # the big ones are: From 0c94d378081783b455ab4e3800696b99af76940c Mon Sep 17 00:00:00 2001 From: Colin Catlin Date: Mon, 13 May 2024 21:30:59 -0500 Subject: [PATCH 9/9] 0.6.13 --- TODO.md | 1 + autots/__init__.py | 2 +- autots/evaluator/auto_ts.py | 26 ++++++++--------- autots/models/base.py | 12 ++++---- autots/models/basics.py | 27 +++++++++++------- autots/models/ensemble.py | 18 ++++++------ autots/models/prophet.py | 25 ++++++++++++---- autots/models/sklearn.py | 7 +++-- autots/tools/fast_kalman.py | 12 ++++++-- autots/tools/seasonal.py | 1 + docs/build/doctrees/environment.pickle | Bin 9694950 -> 9710788 bytes docs/build/doctrees/source/autots.doctree | Bin 981808 -> 982009 bytes .../doctrees/source/autots.evaluator.doctree | Bin 922644 -> 922845 bytes .../doctrees/source/autots.models.doctree | Bin 2518966 -> 2521309 bytes .../doctrees/source/autots.tools.doctree | Bin 1725051 -> 1732071 bytes docs/build/doctrees/source/intro.doctree | Bin 45585 -> 45577 bytes docs/build/html/.buildinfo | 2 +- .../html/_static/documentation_options.js | 2 +- docs/build/html/genindex.html | 24 ++++++++++------ docs/build/html/index.html | 4 +-- docs/build/html/objects.inv | Bin 8064 -> 8097 bytes docs/build/html/py-modindex.html | 4 +-- docs/build/html/search.html | 4 +-- docs/build/html/searchindex.js | 2 +- docs/build/html/source/autots.datasets.html | 4 +-- docs/build/html/source/autots.evaluator.html | 6 ++-- docs/build/html/source/autots.html | 10 +++++-- docs/build/html/source/autots.models.html | 9 +++--- docs/build/html/source/autots.templates.html | 4 +-- docs/build/html/source/autots.tools.html | 26 +++++++++++++++-- docs/build/html/source/intro.html | 6 ++-- docs/build/html/source/modules.html | 4 +-- docs/build/html/source/tutorial.html | 4 +-- docs/conf.py | 2 +- pyproject.toml | 2 +- setup.py | 2 +- 36 files changed, 158 insertions(+), 94 deletions(-) diff --git a/TODO.md b/TODO.md index 524c913b..94b226e3 100644 --- a/TODO.md +++ b/TODO.md @@ -16,6 +16,7 @@ # 0.6.13 🇺🇦 🇺🇦 🇺🇦 * trend_phi directly into Prophet * subset arg to make KalmanStateSpace more scalable to memory +* bug fixes ### Unstable Upstream Pacakges (those that are frequently broken by maintainers) * Pytorch-Forecasting diff --git a/autots/__init__.py b/autots/__init__.py index bde6a9e1..4be1f72b 100644 --- a/autots/__init__.py +++ b/autots/__init__.py @@ -27,7 +27,7 @@ from autots.models.cassandra import Cassandra -__version__ = '0.6.12' +__version__ = '0.6.13' TransformTS = GeneralTransformer diff --git a/autots/evaluator/auto_ts.py b/autots/evaluator/auto_ts.py index 6836c1a1..72232fc8 100644 --- a/autots/evaluator/auto_ts.py +++ b/autots/evaluator/auto_ts.py @@ -418,9 +418,9 @@ def __init__( full_params['transformations'] = transformations full_params['transformation_params'] = transformation_params - self.initial_template.loc[index, 'TransformationParameters'] = ( - json.dumps(full_params) - ) + self.initial_template.loc[ + index, 'TransformationParameters' + ] = json.dumps(full_params) self.regressor_used = False self.grouping_ids = None @@ -1827,10 +1827,10 @@ def _run_template( self.model_count = template_result.model_count # capture results from lower-level template run if "TotalRuntime" in template_result.model_results.columns: - template_result.model_results['TotalRuntime'] = ( - template_result.model_results['TotalRuntime'].fillna( - pd.Timedelta(seconds=60) - ) + template_result.model_results[ + 'TotalRuntime' + ] = template_result.model_results['TotalRuntime'].fillna( + pd.Timedelta(seconds=60) ) else: # trying to catch a rare and sneaky bug (perhaps some variety of beetle?) @@ -1930,9 +1930,9 @@ def _run_validations( frac=0.8, random_state=self.random_seed ).reindex(idx) nan_frac = val_df_train.shape[1] / num_validations - val_df_train.iloc[-2:, int(nan_frac * y) : int(nan_frac * (y + 1))] = ( - np.nan - ) + val_df_train.iloc[ + -2:, int(nan_frac * y) : int(nan_frac * (y + 1)) + ] = np.nan # run validation template on current slice result = self._run_template( @@ -3851,9 +3851,9 @@ def diagnose_params(self, target='runtime', waterfall_plots=True): ) y = pd.json_normalize(json.loads(row["ModelParameters"])) y.index = [row['ID']] - y['Model'] = ( - x # might need to remove this and do analysis independently for each - ) + y[ + 'Model' + ] = x # might need to remove this and do analysis independently for each res.append( pd.DataFrame( { diff --git a/autots/models/base.py b/autots/models/base.py index 00b4de04..0ee1fc3b 100644 --- a/autots/models/base.py +++ b/autots/models/base.py @@ -690,18 +690,18 @@ def long_form_results( value_name=value_name, id_vars="datetime", ).set_index("datetime") - upload_upper[interval_name] = ( - f"{round(100 - ((1- self.prediction_interval)/2) * 100, 0)}%" - ) + upload_upper[ + interval_name + ] = f"{round(100 - ((1- self.prediction_interval)/2) * 100, 0)}%" upload_lower = pd.melt( self.lower_forecast.rename_axis(index='datetime').reset_index(), var_name=id_name, value_name=value_name, id_vars="datetime", ).set_index("datetime") - upload_lower[interval_name] = ( - f"{round(((1- self.prediction_interval)/2) * 100, 0)}%" - ) + upload_lower[ + interval_name + ] = f"{round(((1- self.prediction_interval)/2) * 100, 0)}%" upload = pd.concat([upload, upload_upper, upload_lower], axis=0) if datetime_column is not None: diff --git a/autots/models/basics.py b/autots/models/basics.py index 5e98b6a6..896f9aa9 100644 --- a/autots/models/basics.py +++ b/autots/models/basics.py @@ -2116,7 +2116,7 @@ def fit(self, df, future_regressor=None): if (df.shape[1] % self.subset) != 0: chunks += 1 for x in range(chunks): - subset = df.iloc[:, self.subset * x: (self.subset * (x + 1))] + subset = df.iloc[:, self.subset * x : (self.subset * (x + 1))] self.subset_columns[str(x)] = subset.columns.tolist() self.kf[str(x)] = self._fit(subset, future_regressor=None) else: @@ -2161,12 +2161,8 @@ def cost_function(self, param, df): # covariances=False, ) if self.em_iter is not None: - kf = kf.em( - local[:, : -self.forecast_length], n_iter=self.em_iter - ) - result = kf.predict( - local[:, : -self.forecast_length], self.forecast_length - ) + kf = kf.em(local[:, : -self.forecast_length], n_iter=self.em_iter) + result = kf.predict(local[:, : -self.forecast_length], self.forecast_length) df_smooth = pd.DataFrame( result.observations.mean.T, index=df.index[-self.forecast_length :], @@ -2252,7 +2248,10 @@ def predict( lowers = [] for x in self.subset_columns: current_cols = self.subset_columns[x] - result = self.kf[x].predict(self.df_train.reindex(columns=current_cols).to_numpy().T, forecast_length) + result = self.kf[x].predict( + self.df_train.reindex(columns=current_cols).to_numpy().T, + forecast_length, + ) df = pd.DataFrame( result.observations.mean.T, index=future_index, @@ -2264,8 +2263,12 @@ def predict( uppers.append(df + bound) lowers.append(df - bound) df = pd.concat(forecasts, axis=1).reindex(columns=self.column_names) - upper_forecast = pd.concat(uppers, axis=1).reindex(columns=self.column_names) - lower_forecast = pd.concat(lowers, axis=1).reindex(columns=self.column_names) + upper_forecast = pd.concat(uppers, axis=1).reindex( + columns=self.column_names + ) + lower_forecast = pd.concat(lowers, axis=1).reindex( + columns=self.column_names + ) else: result = self.kf.predict(self.df_train.to_numpy().T, forecast_length) df = pd.DataFrame( @@ -2302,7 +2305,9 @@ def get_new_params(self, method: str = "random"): if method in ['deep']: new_params['subset'] = random.choices([None, 200, 300], [0.3, 0.3, 0.3])[0] else: - new_params['subset'] = random.choices([100, 200, 300], [0.3, 0.3, 0.3])[0] # probably no difference + new_params['subset'] = random.choices([100, 200, 300], [0.3, 0.3, 0.3])[ + 0 + ] # probably no difference return new_params def get_params(self): diff --git a/autots/models/ensemble.py b/autots/models/ensemble.py index 94a8c0c9..fba9a998 100644 --- a/autots/models/ensemble.py +++ b/autots/models/ensemble.py @@ -1838,15 +1838,15 @@ def MosaicEnsemble( f"Mosaic Ensemble failed on model {row[3]} series {row[2]} and period {row[1]} due to missing model: {e} " + mi ) - melted['forecast'] = ( - fore # [forecasts[row[3]][row[2]].iloc[row[1]] for row in melted.itertuples()] - ) - melted['upper_forecast'] = ( - u_fore # [upper_forecasts[row[3]][row[2]].iloc[row[1]] for row in melted.itertuples()] - ) - melted['lower_forecast'] = ( - l_fore # [lower_forecasts[row[3]][row[2]].iloc[row[1]] for row in melted.itertuples()] - ) + melted[ + 'forecast' + ] = fore # [forecasts[row[3]][row[2]].iloc[row[1]] for row in melted.itertuples()] + melted[ + 'upper_forecast' + ] = u_fore # [upper_forecasts[row[3]][row[2]].iloc[row[1]] for row in melted.itertuples()] + melted[ + 'lower_forecast' + ] = l_fore # [lower_forecasts[row[3]][row[2]].iloc[row[1]] for row in melted.itertuples()] forecast_df = melted.pivot( values="forecast", columns="series_id", index="forecast_period" diff --git a/autots/models/prophet.py b/autots/models/prophet.py index e1622cf7..0528bfc6 100644 --- a/autots/models/prophet.py +++ b/autots/models/prophet.py @@ -222,7 +222,11 @@ def seek_the_oracle( future[args['regressor_name']] = a fcst = m.predict(future) # fcst = fcst.tail(forecast_length) # remove the backcast - if self.trend_phi is not None and self.trend_phi != 1 and forecast_length > 2: + if ( + self.trend_phi is not None + and self.trend_phi != 1 + and forecast_length > 2 + ): req_len = fcst.shape[0] - 1 phi_series = pd.Series( [self.trend_phi] * req_len, @@ -237,9 +241,18 @@ def seek_the_oracle( ] ).cumsum() # for now, not doing the dampening on upper and lower bounds as those might still be good to have the full probability - forecast = fcst['trend'] * (1 + fcst["multiplicative_terms"]) + fcst['additive_terms'] - upper_forecast = fcst['trend_upper'] * (1 + fcst["multiplicative_terms_upper"]) + fcst['additive_terms_upper'] - lower_forecast = fcst['trend_lower'] * (1 + fcst["multiplicative_terms_lower"]) + fcst['additive_terms_lower'] + forecast = ( + fcst['trend'] * (1 + fcst["multiplicative_terms"]) + + fcst['additive_terms'] + ) + upper_forecast = ( + fcst['trend_upper'] * (1 + fcst["multiplicative_terms_upper"]) + + fcst['additive_terms_upper'] + ) + lower_forecast = ( + fcst['trend_lower'] * (1 + fcst["multiplicative_terms_lower"]) + + fcst['additive_terms_lower'] + ) else: forecast = fcst['yhat'] lower_forecast = fcst['yhat_lower'] @@ -366,7 +379,9 @@ def get_new_params(self, method: str = 'random'): 'n_changepoints': random.choices( [5, 10, 20, 25, 30, 40, 50], [0.05, 0.1, 0.1, 0.9, 0.1, 0.05, 0.05] )[0], - "trend_phi": random.choices([None, 0.98, 0.999, 0.95, 0.8], [0.8, 0.1, 0.2, 0.1, 0.1])[0], + "trend_phi": random.choices( + [None, 0.98, 0.999, 0.95, 0.8], [0.8, 0.1, 0.2, 0.1, 0.1] + )[0], } def get_params(self): diff --git a/autots/models/sklearn.py b/autots/models/sklearn.py index 9a3f6ebd..268e9c7e 100644 --- a/autots/models/sklearn.py +++ b/autots/models/sklearn.py @@ -264,7 +264,8 @@ def rolling_x_regressor_regressor( X = X.drop(columns=['series_id']) if series_id is not None: hashed = ( - int(hashlib.sha256(str(series_id).encode('utf-8')).hexdigest(), 16) % 10**16 + int(hashlib.sha256(str(series_id).encode('utf-8')).hexdigest(), 16) + % 10**16 ) X['series_id'] = hashed return X @@ -3522,7 +3523,9 @@ def _rbf_kernel(self, x1, x2, gamma): if gamma is None: gamma = 1.0 / x1.shape[1] distance = ( - np.sum(x1**2, 1).reshape(-1, 1) + np.sum(x2**2, 1) - 2 * np.dot(x1, x2.T) + np.sum(x1**2, 1).reshape(-1, 1) + + np.sum(x2**2, 1) + - 2 * np.dot(x1, x2.T) ) return np.exp(-gamma * distance) diff --git a/autots/tools/fast_kalman.py b/autots/tools/fast_kalman.py index f3615888..aa09b69d 100644 --- a/autots/tools/fast_kalman.py +++ b/autots/tools/fast_kalman.py @@ -142,20 +142,24 @@ def ensure_stability(st): st_stable = eigenvectors @ np.diag(stable_eigenvalues) @ np.linalg.inv(eigenvectors) return st_stable.real + def random_matrix(rows, cols, density=0.2): matrix = np.random.randn(rows, cols) sparsity_mask = np.random.rand(rows, cols) < density return np.where(sparsity_mask, matrix, 0) + def random_state_space(tries=10): for _ in range(tries): n_dims = random.choices([1, 2, 3, 4, 8], [0.1, 0.2, 0.3, 0.4, 0.3])[0] st = random_matrix(n_dims, n_dims, density=0.5) st = ensure_stability(st) - obsmod = random_matrix(1, n_dims, density=1.0) # Full observation for simplicity + obsmod = random_matrix( + 1, n_dims, density=1.0 + ) # Full observation for simplicity procnois = np.diag(np.random.exponential(0.01, size=n_dims)).round(3) obsnois = np.random.exponential(1.0) - + if np.all(np.abs(np.linalg.eigvals(st)) < 1): # Check stability return st, procnois, obsmod, obsnois raise ValueError("Failed to generate a stable model after several tries") @@ -1350,12 +1354,14 @@ def douter(a, b): "Outer product, last two axes" return a * b.transpose((0, 2, 1)) + def stable_pinv(A, tol=1e-5, regularization=1e-4): n = A.shape[1] U, s, Vt = np.linalg.svd((A + regularization * np.eye(n)), full_matrices=False) - s_inv = np.where(s > tol, 1/s, 0) + s_inv = np.where(s > tol, 1 / s, 0) return Vt.T @ np.diag(s_inv) @ U.T + def dinv(A): "Matrix inverse applied to last two axes" try: diff --git a/autots/tools/seasonal.py b/autots/tools/seasonal.py index d73dc358..b74aaa95 100644 --- a/autots/tools/seasonal.py +++ b/autots/tools/seasonal.py @@ -596,6 +596,7 @@ def create_seasonality_feature(DTindex, t, seasonality, history_days=None): "other", ] + def random_datepart(method='random'): """New random parameters for seasonality.""" seasonalities = random.choices( diff --git a/docs/build/doctrees/environment.pickle b/docs/build/doctrees/environment.pickle index a51ca5548c3958a7167e003cd71afef5388e9c29..9f01fff89e30446c415baecc621e6a6fe6b8f2d9 100644 GIT binary patch delta 997649 zcmc$HcYIaF_Ah%vdPpUqB#;CMEg{Ki0i-2#MM~%jl0*oY03jiOVk0PmG)Xv+u|g2k zdqt&lETEt$V8I}Ypn?JHUKP;m^{T-8t~Iky3GmB%@AE#N_s`jTX4b6v&YIb4)~q#q zpKnfm*!aN7+QwVY8-{Ji1!GRAVRKGx;y~ibCgs7nL^d1hkISS4BgnbBU7XR>S(Vb% zSmT`3uBG#rcFE3{CWWj{iO*_MK5Opm$%PpObLY&OQ@&-a!N{opI@U!D_Y9@!lnrUmjv$P<(6Y{t&B#_6)B10Z{d6~rCESM 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When it is not found, a full rebuild will be done. -config: 18a08ac66d0dab3da1a184ed771beee5 +config: 5ed6f58ce47b614a3ce58fe2cd60c840 tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/docs/build/html/_static/documentation_options.js b/docs/build/html/_static/documentation_options.js index e4673028..fb0668af 100644 --- a/docs/build/html/_static/documentation_options.js +++ b/docs/build/html/_static/documentation_options.js @@ -1,5 +1,5 @@ const DOCUMENTATION_OPTIONS = { - VERSION: '0.6.11', + VERSION: '0.6.13', LANGUAGE: 'en', COLLAPSE_INDEX: false, BUILDER: 'html', diff --git a/docs/build/html/genindex.html b/docs/build/html/genindex.html index 98e3624b..9fef2924 100644 --- a/docs/build/html/genindex.html +++ b/docs/build/html/genindex.html @@ -13,10 +13,10 @@ gtag('config', 'G-P2KLF8302E'); - Index — AutoTS 0.6.11 documentation + Index — AutoTS 0.6.13 documentation - + @@ -874,6 +874,8 @@

    E

  • EnsembleTemplateGenerator() (in module autots.models.ensemble)
  • ensure_matrix() (in module autots.tools.fast_kalman) +
    • +
  • ensure_stability() (in module autots.tools.fast_kalman)
  • error_correlations() (in module autots.evaluator.auto_ts)
    • @@ -881,14 +883,14 @@

    E

  • evaluate() (autots.models.base.PredictionObject method), [1]
    • + + - + -
    • retrieve_validation_forecasts() (autots.AutoTS method) - +
      • set_limit_forecast() (in module autots.evaluator.event_forecasting)
      • set_limit_forecast_historic() (in module autots.evaluator.event_forecasting) @@ -2995,6 +3001,8 @@

        S

      • spl() (in module autots.evaluator.metrics)
      • split_digits_and_non_digits() (in module autots.tools.shaping) +
        • +
      • stable_pinv() (in module autots.tools.fast_kalman)
      • StatsmodelsFilter (class in autots.tools.transform)
        • diff --git a/docs/build/html/index.html b/docs/build/html/index.html index 8e1b6acf..95101947 100644 --- a/docs/build/html/index.html +++ b/docs/build/html/index.html @@ -14,10 +14,10 @@ gtag('config', 'G-P2KLF8302E'); - AutoTS — AutoTS 0.6.11 documentation + AutoTS — AutoTS 0.6.13 documentation - + diff --git a/docs/build/html/objects.inv b/docs/build/html/objects.inv index 9edc74a54e3dfc427c0c6e41cc921f9e5f0909ee..525ef468f7614660531b35d826861f4b708c4e58 100644 GIT binary patch delta 8029 zcmV-jAEMxZKcPR6iGNdU%gKr5Xe3Q$cVBQI65Wl61Ze_nN&fT$@J=?_4FZ4cLt;tY z_!WRE0EJ5dv{Jc}Z;IEyXL(Yk?9HFv$h<0J_FuIABs!=p{g39e@Bj17_usXDz7lyx z)7KA7u~_A$0mLbl^3B_&^y>+ur2e7K08@uN*$mim&(wrAe}5x&0r-xy(gV0>iU{^h z3R==kdVq>F_bHf+^O6yjcct5c(*Hc%Z%}Fd{pgX#Ph_X1##E}!;-W_;714&qQj?Y{ zRr1G5vF*tT6Ov_*+Lk>zf@a=~!3?}8tR#|^oJo?=g85YLm{r?|7fh8@NUigXDxM3U z(k46d%o9dLm4E5X#cm8v_ac|fq46~U_EJ!hkV9VbZ@Ex3bwcE&B2#O2LMWCMDOC;% 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method)": [[3, "autots.evaluator.anomaly_detector.AnomalyDetector.fit"]], "fit() (autots.evaluator.anomaly_detector.holidaydetector method)": [[3, "autots.evaluator.anomaly_detector.HolidayDetector.fit"]], "fit() (autots.evaluator.auto_model.modelprediction method)": [[3, "autots.evaluator.auto_model.ModelPrediction.fit"]], "fit() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.fit"]], "fit() (autots.evaluator.event_forecasting.eventriskforecast method)": [[3, "autots.evaluator.event_forecasting.EventRiskForecast.fit"], [3, "id0"]], "fit_anomaly_classifier() (autots.evaluator.anomaly_detector.anomalydetector method)": [[3, "autots.evaluator.anomaly_detector.AnomalyDetector.fit_anomaly_classifier"]], "fit_data() (autots.evaluator.auto_model.modelprediction method)": [[3, "autots.evaluator.auto_model.ModelPrediction.fit_data"]], "fit_data() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.fit_data"]], "fit_predict() 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"autots.evaluator.metrics.wasserstein"]], "arch (class in autots.models.arch)": [[4, "autots.models.arch.ARCH"]], "ardl (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.ARDL"]], "arima (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.ARIMA"]], "averagevaluenaive (class in autots.models.basics)": [[4, "autots.models.basics.AverageValueNaive"]], "balltreemultivariatemotif (class in autots.models.basics)": [[4, "autots.models.basics.BallTreeMultivariateMotif"]], "bayesianmultioutputregression (class in autots.models.cassandra)": [[4, "autots.models.cassandra.BayesianMultiOutputRegression"]], "bestnensemble() (in module autots.models.ensemble)": [[4, "autots.models.ensemble.BestNEnsemble"]], "cassandra (class in autots.models.cassandra)": [[4, "autots.models.cassandra.Cassandra"]], "componentanalysis (class in autots.models.sklearn)": [[4, "autots.models.sklearn.ComponentAnalysis"]], "constantnaive (class in autots.models.basics)": [[4, 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autots.models.basics)": [[4, "autots.models.basics.LastValueNaive"]], "mar (class in autots.models.matrix_var)": [[4, "autots.models.matrix_var.MAR"]], "mlensemble (class in autots.models.mlensemble)": [[4, "autots.models.mlensemble.MLEnsemble"]], "metricmotif (class in autots.models.basics)": [[4, "autots.models.basics.MetricMotif"]], "modelobject (class in autots.models.base)": [[4, "autots.models.base.ModelObject"]], "mosaicensemble() (in module autots.models.ensemble)": [[4, "autots.models.ensemble.MosaicEnsemble"]], "motif (class in autots.models.basics)": [[4, "autots.models.basics.Motif"]], "motifsimulation (class in autots.models.basics)": [[4, "autots.models.basics.MotifSimulation"]], "multivariateregression (class in autots.models.sklearn)": [[4, "autots.models.sklearn.MultivariateRegression"]], "nvar (class in autots.models.basics)": [[4, "autots.models.basics.NVAR"]], "neuralforecast (class in autots.models.neural_forecast)": [[4, "autots.models.neural_forecast.NeuralForecast"]], "neuralprophet (class in autots.models.prophet)": [[4, "autots.models.prophet.NeuralProphet"]], "predictionobject (class in autots.models.base)": [[4, "autots.models.base.PredictionObject"]], "preprocessingregression (class in autots.models.sklearn)": [[4, "autots.models.sklearn.PreprocessingRegression"]], "pytorchforecasting (class in autots.models.pytorch)": [[4, "autots.models.pytorch.PytorchForecasting"]], "rrvar (class in autots.models.matrix_var)": [[4, "autots.models.matrix_var.RRVAR"]], "rollingregression (class in autots.models.sklearn)": [[4, "autots.models.sklearn.RollingRegression"]], "seasonalnaive (class in autots.models.basics)": [[4, "autots.models.basics.SeasonalNaive"]], "seasonalitymotif (class in autots.models.basics)": [[4, "autots.models.basics.SeasonalityMotif"]], "sectionalmotif (class in autots.models.basics)": [[4, "autots.models.basics.SectionalMotif"]], "tfpregression (class in autots.models.tfp)": [[4, "autots.models.tfp.TFPRegression"]], "tfpregressor (class in autots.models.tfp)": [[4, "autots.models.tfp.TFPRegressor"]], "tmf (class in autots.models.matrix_var)": [[4, "autots.models.matrix_var.TMF"]], "tensorflowsts (class in autots.models.tfp)": [[4, "autots.models.tfp.TensorflowSTS"]], "theta (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.Theta"]], "tide (class in autots.models.tide)": [[4, "autots.models.tide.TiDE"]], "timecovariates (class in autots.models.tide)": [[4, "autots.models.tide.TimeCovariates"]], "timeseriesdata (class in autots.models.tide)": [[4, "autots.models.tide.TimeSeriesdata"]], "transformer (class in autots.models.dnn)": [[4, "autots.models.dnn.Transformer"]], "univariateregression (class in autots.models.sklearn)": [[4, "autots.models.sklearn.UnivariateRegression"]], "unobservedcomponents (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.UnobservedComponents"]], "var (class in autots.models.statsmodels)": [[4, 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[[4, "autots.models.sklearn.DatepartRegression.fit_data"]], "fit_data() (autots.models.sklearn.multivariateregression method)": [[4, "autots.models.sklearn.MultivariateRegression.fit_data"]], "fit_data() (autots.models.sklearn.preprocessingregression method)": [[4, "autots.models.sklearn.PreprocessingRegression.fit_data"]], "fit_data() (autots.models.sklearn.windowregression method)": [[4, "autots.models.sklearn.WindowRegression.fit_data"]], "fit_linear_model() (in module autots.models.cassandra)": [[4, "autots.models.cassandra.fit_linear_model"]], "forecast (autots.models.base.predictionobject attribute)": [[4, "autots.models.base.PredictionObject.forecast"]], "generalize_horizontal() (in module autots.models.ensemble)": [[4, "autots.models.ensemble.generalize_horizontal"]], "generate_psi() (in module autots.models.matrix_var)": [[4, "autots.models.matrix_var.generate_Psi"]], "generate_classifier_params() (in module autots.models.sklearn)": [[4, 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(autots.models.cassandra.cassandra. attribute)": [[4, "autots.models.cassandra.Cassandra..predict_x_array"]], "predicted_trend (autots.models.cassandra.cassandra. attribute)": [[4, "autots.models.cassandra.Cassandra..predicted_trend"]], "process_components() (autots.models.cassandra.cassandra method)": [[4, "autots.models.cassandra.Cassandra.process_components"]], "process_mosaic_arrays() (in module autots.models.ensemble)": [[4, "autots.models.ensemble.process_mosaic_arrays"]], "retrieve_classifier() (in module autots.models.sklearn)": [[4, "autots.models.sklearn.retrieve_classifier"]], "retrieve_regressor() (in module autots.models.sklearn)": [[4, "autots.models.sklearn.retrieve_regressor"]], "return_components() (autots.models.cassandra.cassandra method)": [[4, "autots.models.cassandra.Cassandra.return_components"], [4, "id11"]], "rmse() (in module autots.models.tide)": [[4, "autots.models.tide.rmse"]], "rolling_trend() (autots.models.cassandra.cassandra method)": [[4, 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"autots.evaluator.benchmark": [[3, "module-autots.evaluator.benchmark"]], "autots.evaluator.event_forecasting": [[3, "module-autots.evaluator.event_forecasting"]], "autots.evaluator.metrics": [[3, "module-autots.evaluator.metrics"]], "autots.evaluator.validation": [[3, "module-autots.evaluator.validation"]], "back_forecast() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.back_forecast"]], "back_forecast() (in module autots.evaluator.auto_model)": [[3, "autots.evaluator.auto_model.back_forecast"]], "best_model (autots.evaluator.auto_ts.autots attribute)": [[3, "autots.evaluator.auto_ts.AutoTS.best_model"]], "best_model_ensemble (autots.evaluator.auto_ts.autots attribute)": [[3, "autots.evaluator.auto_ts.AutoTS.best_model_ensemble"]], "best_model_name (autots.evaluator.auto_ts.autots attribute)": [[3, "autots.evaluator.auto_ts.AutoTS.best_model_name"]], "best_model_params (autots.evaluator.auto_ts.autots attribute)": [[3, 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"linearity() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.linearity"]], "list_failed_model_types() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.list_failed_model_types"]], "load() (autots.evaluator.auto_model.templateevalobject method)": [[3, "autots.evaluator.auto_model.TemplateEvalObject.load"]], "load_template() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.load_template"]], "mae() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.mae"]], "mda() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.mda"]], "mean_absolute_differential_error() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.mean_absolute_differential_error"]], "mean_absolute_error() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.mean_absolute_error"]], "medae() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.medae"]], "median_absolute_error() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.median_absolute_error"]], "mlvb() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.mlvb"]], "model_forecast() (in module autots.evaluator.auto_model)": [[3, "autots.evaluator.auto_model.model_forecast"]], "model_results (autots.evaluator.auto_ts.autots.initial_results attribute)": [[3, "autots.evaluator.auto_ts.AutoTS.initial_results.model_results"]], "mosaic_to_df() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.mosaic_to_df"]], "mqae() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.mqae"]], "msle() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.msle"]], "numpy_ffill() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.numpy_ffill"]], "oda() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.oda"]], "parse_best_model() (autots.evaluator.auto_ts.autots 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(autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.plot_per_series_error"]], "plot_per_series_mape() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.plot_per_series_mape"]], "plot_per_series_smape() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.plot_per_series_smape"]], "plot_series_corr() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.plot_series_corr"]], "plot_transformer_failure_rate() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.plot_transformer_failure_rate"]], "plot_validations() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.plot_validations"]], "precomp_wasserstein() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.precomp_wasserstein"]], "predict() (autots.evaluator.auto_model.modelprediction method)": [[3, 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"autots.evaluator.auto_ts.AutoTS.results"]], "retrieve_validation_forecasts() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.retrieve_validation_forecasts"]], "rmse() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.rmse"]], "root_mean_square_error() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.root_mean_square_error"]], "rps() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.rps"]], "run() (autots.evaluator.benchmark.benchmark method)": [[3, "autots.evaluator.benchmark.Benchmark.run"]], "save() (autots.evaluator.auto_model.templateevalobject method)": [[3, "autots.evaluator.auto_model.TemplateEvalObject.save"]], "save_template() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.save_template"]], "scaled_pinball_loss() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.scaled_pinball_loss"]], "score_per_series (autots.evaluator.auto_ts.autots attribute)": [[3, "autots.evaluator.auto_ts.AutoTS.score_per_series"]], "score_to_anomaly() (autots.evaluator.anomaly_detector.anomalydetector method)": [[3, "autots.evaluator.anomaly_detector.AnomalyDetector.score_to_anomaly"]], "set_limit() (autots.evaluator.event_forecasting.eventriskforecast method)": [[3, "autots.evaluator.event_forecasting.EventRiskForecast.set_limit"]], "set_limit() (autots.evaluator.event_forecasting.eventriskforecast static method)": [[3, "id13"]], "set_limit_forecast() (in module autots.evaluator.event_forecasting)": [[3, "autots.evaluator.event_forecasting.set_limit_forecast"]], "set_limit_forecast_historic() (in module autots.evaluator.event_forecasting)": [[3, "autots.evaluator.event_forecasting.set_limit_forecast_historic"]], "smape() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.smape"]], "smoothness() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.smoothness"]], "spl() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.spl"]], "symmetric_mean_absolute_percentage_error() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.symmetric_mean_absolute_percentage_error"]], "threshold_loss() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.threshold_loss"]], "trans_dict_recomb() (in module autots.evaluator.auto_model)": [[3, "autots.evaluator.auto_model.trans_dict_recomb"]], "unpack_ensemble_models() (in module autots.evaluator.auto_model)": [[3, "autots.evaluator.auto_model.unpack_ensemble_models"]], "unsorted_wasserstein() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.unsorted_wasserstein"]], "validate_num_validations() (in module autots.evaluator.validation)": [[3, "autots.evaluator.validation.validate_num_validations"]], "validation_agg() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.validation_agg"]], "validation_aggregation() (in module autots.evaluator.auto_model)": [[3, "autots.evaluator.auto_model.validation_aggregation"]], "wasserstein() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.wasserstein"]], "arch (class in autots.models.arch)": [[4, "autots.models.arch.ARCH"]], "ardl (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.ARDL"]], "arima (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.ARIMA"]], "averagevaluenaive (class in autots.models.basics)": [[4, "autots.models.basics.AverageValueNaive"]], "balltreemultivariatemotif (class in autots.models.basics)": [[4, "autots.models.basics.BallTreeMultivariateMotif"]], "bayesianmultioutputregression (class in autots.models.cassandra)": [[4, "autots.models.cassandra.BayesianMultiOutputRegression"]], "bestnensemble() (in module autots.models.ensemble)": [[4, "autots.models.ensemble.BestNEnsemble"]], "cassandra (class in autots.models.cassandra)": [[4, "autots.models.cassandra.Cassandra"]], "componentanalysis (class in autots.models.sklearn)": [[4, "autots.models.sklearn.ComponentAnalysis"]], "constantnaive (class in autots.models.basics)": [[4, "autots.models.basics.ConstantNaive"]], "dmd (class in autots.models.matrix_var)": [[4, "autots.models.matrix_var.DMD"]], "datepartregression (class in autots.models.sklearn)": [[4, "autots.models.sklearn.DatepartRegression"]], "distensemble() (in module autots.models.ensemble)": [[4, "autots.models.ensemble.DistEnsemble"]], "dynamicfactor (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.DynamicFactor"]], "dynamicfactormq (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.DynamicFactorMQ"]], "ets (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.ETS"]], "ensembleforecast() (in module autots.models.ensemble)": [[4, "autots.models.ensemble.EnsembleForecast"]], "ensembletemplategenerator() (in module autots.models.ensemble)": [[4, "autots.models.ensemble.EnsembleTemplateGenerator"]], "fbprophet (class in autots.models.prophet)": [[4, "autots.models.prophet.FBProphet"]], "fft (class in autots.models.basics)": [[4, "autots.models.basics.FFT"]], "glm (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.GLM"]], "gls (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.GLS"]], "gluonts (class in autots.models.gluonts)": [[4, "autots.models.gluonts.GluonTS"]], "greykite (class in autots.models.greykite)": [[4, "autots.models.greykite.Greykite"]], "hdistensemble() (in module autots.models.ensemble)": [[4, "autots.models.ensemble.HDistEnsemble"]], "horizontalensemble() (in module autots.models.ensemble)": [[4, "autots.models.ensemble.HorizontalEnsemble"]], "horizontaltemplategenerator() (in module autots.models.ensemble)": [[4, "autots.models.ensemble.HorizontalTemplateGenerator"]], "kalmanstatespace (class in autots.models.basics)": [[4, "autots.models.basics.KalmanStateSpace"]], "kerasrnn (class in autots.models.dnn)": [[4, "autots.models.dnn.KerasRNN"]], "latc (class in autots.models.matrix_var)": [[4, "autots.models.matrix_var.LATC"]], "lastvaluenaive (class in autots.models.basics)": [[4, "autots.models.basics.LastValueNaive"]], "mar (class in autots.models.matrix_var)": [[4, "autots.models.matrix_var.MAR"]], "mlensemble (class in autots.models.mlensemble)": [[4, "autots.models.mlensemble.MLEnsemble"]], "metricmotif (class in autots.models.basics)": [[4, "autots.models.basics.MetricMotif"]], "modelobject (class in autots.models.base)": [[4, "autots.models.base.ModelObject"]], "mosaicensemble() (in module autots.models.ensemble)": [[4, "autots.models.ensemble.MosaicEnsemble"]], "motif (class in autots.models.basics)": [[4, "autots.models.basics.Motif"]], "motifsimulation (class in autots.models.basics)": [[4, "autots.models.basics.MotifSimulation"]], "multivariateregression (class in autots.models.sklearn)": [[4, "autots.models.sklearn.MultivariateRegression"]], "nvar (class in autots.models.basics)": [[4, "autots.models.basics.NVAR"]], "neuralforecast (class in autots.models.neural_forecast)": [[4, "autots.models.neural_forecast.NeuralForecast"]], "neuralprophet (class in autots.models.prophet)": [[4, "autots.models.prophet.NeuralProphet"]], "predictionobject (class in autots.models.base)": [[4, "autots.models.base.PredictionObject"]], "preprocessingregression (class in autots.models.sklearn)": [[4, "autots.models.sklearn.PreprocessingRegression"]], "pytorchforecasting (class in autots.models.pytorch)": [[4, "autots.models.pytorch.PytorchForecasting"]], "rrvar (class in autots.models.matrix_var)": [[4, "autots.models.matrix_var.RRVAR"]], "rollingregression (class in autots.models.sklearn)": [[4, "autots.models.sklearn.RollingRegression"]], "seasonalnaive (class in autots.models.basics)": [[4, "autots.models.basics.SeasonalNaive"]], "seasonalitymotif (class in autots.models.basics)": [[4, "autots.models.basics.SeasonalityMotif"]], "sectionalmotif (class in autots.models.basics)": [[4, "autots.models.basics.SectionalMotif"]], "tfpregression (class in autots.models.tfp)": [[4, "autots.models.tfp.TFPRegression"]], "tfpregressor (class in autots.models.tfp)": [[4, "autots.models.tfp.TFPRegressor"]], "tmf (class in autots.models.matrix_var)": [[4, "autots.models.matrix_var.TMF"]], "tensorflowsts (class in autots.models.tfp)": [[4, "autots.models.tfp.TensorflowSTS"]], "theta (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.Theta"]], "tide (class in autots.models.tide)": [[4, "autots.models.tide.TiDE"]], "timecovariates (class in autots.models.tide)": [[4, "autots.models.tide.TimeCovariates"]], "timeseriesdata (class in autots.models.tide)": [[4, "autots.models.tide.TimeSeriesdata"]], "transformer (class in autots.models.dnn)": [[4, "autots.models.dnn.Transformer"]], "univariateregression (class in autots.models.sklearn)": [[4, 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"generate_psi() (in module autots.models.matrix_var)": [[4, "autots.models.matrix_var.generate_Psi"]], "generate_classifier_params() (in module autots.models.sklearn)": [[4, "autots.models.sklearn.generate_classifier_params"]], "generate_crosshair_score() (in module autots.models.ensemble)": [[4, "autots.models.ensemble.generate_crosshair_score"]], "generate_crosshair_score_list() (in module autots.models.ensemble)": [[4, "autots.models.ensemble.generate_crosshair_score_list"]], "generate_mosaic_template() (in module autots.models.ensemble)": [[4, "autots.models.ensemble.generate_mosaic_template"]], "generate_regressor_params() (in module autots.models.sklearn)": [[4, "autots.models.sklearn.generate_regressor_params"]], "get_holidays() (in module autots.models.tide)": [[4, "autots.models.tide.get_HOLIDAYS"]], "get_covariates() (autots.models.tide.timecovariates method)": [[4, "autots.models.tide.TimeCovariates.get_covariates"]], "get_new_params() (autots.models.arch.arch method)": [[4, 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"transform() (autots.tools.transform.anomalyremoval method)": [[6, "autots.tools.transform.AnomalyRemoval.transform"]], "transform() (autots.tools.transform.bkbandpassfilter method)": [[6, "autots.tools.transform.BKBandpassFilter.transform"]], "transform() (autots.tools.transform.btcd method)": [[6, "autots.tools.transform.BTCD.transform"]], "transform() (autots.tools.transform.centerlastvalue method)": [[6, "autots.tools.transform.CenterLastValue.transform"]], "transform() (autots.tools.transform.centersplit method)": [[6, "autots.tools.transform.CenterSplit.transform"]], "transform() (autots.tools.transform.clipoutliers method)": [[6, "autots.tools.transform.ClipOutliers.transform"]], "transform() (autots.tools.transform.cointegration method)": [[6, "autots.tools.transform.Cointegration.transform"]], "transform() (autots.tools.transform.cumsumtransformer method)": [[6, "autots.tools.transform.CumSumTransformer.transform"]], "transform() (autots.tools.transform.datepartregressiontransformer method)": [[6, "autots.tools.transform.DatepartRegressionTransformer.transform"]], "transform() (autots.tools.transform.detrend method)": [[6, "autots.tools.transform.Detrend.transform"]], "transform() (autots.tools.transform.diffsmoother method)": [[6, "autots.tools.transform.DiffSmoother.transform"]], "transform() (autots.tools.transform.differencedtransformer method)": [[6, "autots.tools.transform.DifferencedTransformer.transform"]], "transform() (autots.tools.transform.discretize method)": [[6, "autots.tools.transform.Discretize.transform"]], "transform() (autots.tools.transform.ewmafilter method)": [[6, "autots.tools.transform.EWMAFilter.transform"]], "transform() (autots.tools.transform.emptytransformer method)": [[6, "autots.tools.transform.EmptyTransformer.transform"]], "transform() (autots.tools.transform.fftdecomposition method)": [[6, "autots.tools.transform.FFTDecomposition.transform"]], "transform() (autots.tools.transform.fftfilter method)": [[6, "autots.tools.transform.FFTFilter.transform"]], "transform() (autots.tools.transform.fastica method)": [[6, "autots.tools.transform.FastICA.transform"]], "transform() (autots.tools.transform.generaltransformer method)": [[6, "autots.tools.transform.GeneralTransformer.transform"]], "transform() (autots.tools.transform.hpfilter method)": [[6, "autots.tools.transform.HPFilter.transform"]], "transform() (autots.tools.transform.historicvalues method)": [[6, "autots.tools.transform.HistoricValues.transform"]], "transform() (autots.tools.transform.holidaytransformer method)": [[6, "autots.tools.transform.HolidayTransformer.transform"]], "transform() (autots.tools.transform.intermittentoccurrence method)": [[6, "autots.tools.transform.IntermittentOccurrence.transform"]], "transform() (autots.tools.transform.kalmansmoothing method)": [[6, "autots.tools.transform.KalmanSmoothing.transform"]], "transform() (autots.tools.transform.levelshiftmagic method)": [[6, "autots.tools.transform.LevelShiftMagic.transform"]], "transform() (autots.tools.transform.locallineartrend method)": [[6, "autots.tools.transform.LocalLinearTrend.transform"]], "transform() (autots.tools.transform.meandifference method)": [[6, "autots.tools.transform.MeanDifference.transform"]], "transform() (autots.tools.transform.pca method)": [[6, "autots.tools.transform.PCA.transform"]], "transform() (autots.tools.transform.pctchangetransformer method)": [[6, "autots.tools.transform.PctChangeTransformer.transform"]], "transform() (autots.tools.transform.positiveshift method)": [[6, "autots.tools.transform.PositiveShift.transform"]], "transform() (autots.tools.transform.regressionfilter method)": [[6, "autots.tools.transform.RegressionFilter.transform"]], "transform() (autots.tools.transform.replaceconstant method)": [[6, "autots.tools.transform.ReplaceConstant.transform"]], "transform() (autots.tools.transform.rollingmeantransformer method)": [[6, "autots.tools.transform.RollingMeanTransformer.transform"]], "transform() (autots.tools.transform.round method)": [[6, "autots.tools.transform.Round.transform"]], "transform() (autots.tools.transform.stlfilter method)": [[6, "autots.tools.transform.STLFilter.transform"]], "transform() (autots.tools.transform.scipyfilter method)": [[6, "autots.tools.transform.ScipyFilter.transform"]], "transform() (autots.tools.transform.seasonaldifference method)": [[6, "autots.tools.transform.SeasonalDifference.transform"]], "transform() (autots.tools.transform.sintrend method)": [[6, "autots.tools.transform.SinTrend.transform"]], "transform() (autots.tools.transform.slice method)": [[6, "autots.tools.transform.Slice.transform"]], "transform() (autots.tools.transform.statsmodelsfilter method)": [[6, "autots.tools.transform.StatsmodelsFilter.transform"]], "transformer_list_to_dict() (in module autots.tools.transform)": [[6, "autots.tools.transform.transformer_list_to_dict"]], "trimmed_mean() (in module autots.tools.percentile)": [[6, "autots.tools.percentile.trimmed_mean"]], "unvectorize_state() (autots.tools.fast_kalman.gaussian method)": [[6, "autots.tools.fast_kalman.Gaussian.unvectorize_state"]], "unvectorize_vars() (autots.tools.fast_kalman.gaussian method)": [[6, "autots.tools.fast_kalman.Gaussian.unvectorize_vars"]], "update() (autots.tools.fast_kalman.kalmanfilter method)": [[6, "autots.tools.fast_kalman.KalmanFilter.update"]], "update() (in module autots.tools.fast_kalman)": [[6, "autots.tools.fast_kalman.update"]], "update_with_nan_check() (in module autots.tools.fast_kalman)": [[6, "autots.tools.fast_kalman.update_with_nan_check"]], "values_to_anomalies() (in module autots.tools.anomaly_utils)": [[6, "autots.tools.anomaly_utils.values_to_anomalies"]], "wide_to_3d() (in module autots.tools.shaping)": [[6, "autots.tools.shaping.wide_to_3d"]], "window_id_maker() (in module autots.tools.window_functions)": [[6, "autots.tools.window_functions.window_id_maker"]], "window_lin_reg() (in module autots.tools.window_functions)": [[6, "autots.tools.window_functions.window_lin_reg"]], "window_lin_reg_mean() (in module autots.tools.window_functions)": [[6, "autots.tools.window_functions.window_lin_reg_mean"]], "window_lin_reg_mean_no_nan() (in module autots.tools.window_functions)": [[6, "autots.tools.window_functions.window_lin_reg_mean_no_nan"]], "window_maker() (in module autots.tools.window_functions)": [[6, "autots.tools.window_functions.window_maker"]], "window_maker_2() (in module autots.tools.window_functions)": [[6, "autots.tools.window_functions.window_maker_2"]], "window_maker_3() (in module autots.tools.window_functions)": [[6, "autots.tools.window_functions.window_maker_3"]], "window_sum_mean() (in module autots.tools.window_functions)": [[6, "autots.tools.window_functions.window_sum_mean"]], "window_sum_mean_nan_tail() (in module autots.tools.window_functions)": [[6, "autots.tools.window_functions.window_sum_mean_nan_tail"]], "window_sum_nan_mean() (in module autots.tools.window_functions)": [[6, "autots.tools.window_functions.window_sum_nan_mean"]], "zscore_survival_function() (in module autots.tools.anomaly_utils)": [[6, "autots.tools.anomaly_utils.zscore_survival_function"]]}}) \ No newline at end of file diff --git a/docs/build/html/source/autots.datasets.html b/docs/build/html/source/autots.datasets.html index 7acf466a..281f8ca8 100644 --- a/docs/build/html/source/autots.datasets.html +++ b/docs/build/html/source/autots.datasets.html @@ -14,10 +14,10 @@ gtag('config', 'G-P2KLF8302E'); - autots.datasets package — AutoTS 0.6.11 documentation + autots.datasets package — AutoTS 0.6.13 documentation - + diff --git a/docs/build/html/source/autots.evaluator.html b/docs/build/html/source/autots.evaluator.html index b3ab8fd3..29e50184 100644 --- a/docs/build/html/source/autots.evaluator.html +++ b/docs/build/html/source/autots.evaluator.html @@ -14,10 +14,10 @@ gtag('config', 'G-P2KLF8302E'); - autots.evaluator package — AutoTS 0.6.11 documentation + autots.evaluator package — AutoTS 0.6.13 documentation - + @@ -782,7 +782,7 @@

        Submodules
        -export_template(filename=None, models: str = 'best', n: int = 40, max_per_model_class: int | None = None, include_results: bool = False, unpack_ensembles: bool = False, min_metrics: list = ['smape', 'spl'], max_metrics: list | None = None)¶
        +export_template(filename=None, models: str = 'best', n: int = 40, max_per_model_class: int | None = None, include_results: bool = False, unpack_ensembles: bool = False, min_metrics: list = ['smape', 'spl', 'wasserstein', 'mle', 'imle', 'ewmae'], max_metrics: list | None = None)¶

        Export top results as a reusable template.

        Parameters:
        diff --git a/docs/build/html/source/autots.html b/docs/build/html/source/autots.html index e4246869..3d583d27 100644 --- a/docs/build/html/source/autots.html +++ b/docs/build/html/source/autots.html @@ -14,10 +14,10 @@ gtag('config', 'G-P2KLF8302E'); - autots package — AutoTS 0.6.11 documentation + autots package — AutoTS 0.6.13 documentation - + @@ -956,14 +956,18 @@

        Subpackagesdouter()
      • em_initial_state()
      • ensure_matrix()
        • +
      • ensure_stability()
      • holt_winters_damped_matrices()
      • new_kalman_params()
      • predict()
      • predict_observation()
      • priv_smooth()
      • priv_update_with_nan_check()
        • +
      • random_matrix()
      • random_state_space()
        • +
      • random_state_space_original()
      • smooth()
        • +
      • stable_pinv()
      • update()
      • update_with_nan_check()

      @@ -1813,7 +1817,7 @@

      Subpackages
      -export_template(filename=None, models: str = 'best', n: int = 40, max_per_model_class: int | None = None, include_results: bool = False, unpack_ensembles: bool = False, min_metrics: list = ['smape', 'spl'], max_metrics: list | None = None)¶
      +export_template(filename=None, models: str = 'best', n: int = 40, max_per_model_class: int | None = None, include_results: bool = False, unpack_ensembles: bool = False, min_metrics: list = ['smape', 'spl', 'wasserstein', 'mle', 'imle', 'ewmae'], max_metrics: list | None = None)¶

      Export top results as a reusable template.

      Parameters:
      diff --git a/docs/build/html/source/autots.models.html b/docs/build/html/source/autots.models.html index 6e5f285e..1252d33e 100644 --- a/docs/build/html/source/autots.models.html +++ b/docs/build/html/source/autots.models.html @@ -14,10 +14,10 @@ gtag('config', 'G-P2KLF8302E'); - autots.models package — AutoTS 0.6.11 documentation + autots.models package — AutoTS 0.6.13 documentation - + @@ -748,7 +748,7 @@

      Submodules
      -class autots.models.basics.KalmanStateSpace(name: str = 'KalmanStateSpace', frequency: str = 'infer', prediction_interval: float = 0.9, holiday_country: str = 'US', random_seed: int = 2020, verbose: int = 0, state_transition=[[1, 1], [0, 1]], process_noise=[[0.1, 0.0], [0.0, 0.01]], observation_model=[[1, 0]], observation_noise: float = 1.0, em_iter: int = 10, model_name: str = 'undefined', forecast_length: int | None = None, **kwargs)¶
      +class autots.models.basics.KalmanStateSpace(name: str = 'KalmanStateSpace', frequency: str = 'infer', prediction_interval: float = 0.9, holiday_country: str = 'US', random_seed: int = 2020, verbose: int = 0, state_transition=[[1, 1], [0, 1]], process_noise=[[0.1, 0.0], [0.0, 0.01]], observation_model=[[1, 0]], observation_noise: float = 1.0, em_iter: int = 10, model_name: str = 'undefined', forecast_length: int | None = None, subset=None, **kwargs)¶

      Bases: ModelObject

      Forecast using a state space model solved by a Kalman Filter.

      @@ -757,6 +757,7 @@

      Submodules
      -class autots.models.prophet.FBProphet(name: str = 'FBProphet', frequency: str = 'infer', prediction_interval: float = 0.9, holiday: bool = False, regression_type: str | None = None, holiday_country: str = 'US', yearly_seasonality='auto', weekly_seasonality='auto', daily_seasonality='auto', growth: str = 'linear', n_changepoints: int = 25, changepoint_prior_scale: float = 0.05, seasonality_mode: str = 'additive', changepoint_range: float = 0.8, seasonality_prior_scale: float = 10.0, holidays_prior_scale: float = 10.0, random_seed: int = 2020, verbose: int = 0, n_jobs: int | None = None)¶
      +class autots.models.prophet.FBProphet(name: str = 'FBProphet', frequency: str = 'infer', prediction_interval: float = 0.9, holiday: bool = False, regression_type: str | None = None, holiday_country: str = 'US', yearly_seasonality='auto', weekly_seasonality='auto', daily_seasonality='auto', growth: str = 'linear', n_changepoints: int = 25, changepoint_prior_scale: float = 0.05, seasonality_mode: str = 'additive', changepoint_range: float = 0.8, seasonality_prior_scale: float = 10.0, holidays_prior_scale: float = 10.0, trend_phi: float = 1, random_seed: int = 2020, verbose: int = 0, n_jobs: int | None = None)¶

      Bases: ModelObject

      Facebook’s Prophet

      ‘thou shall count to 3, no more, no less, 3 shall be the number thou shall count, and the number of the counting diff --git a/docs/build/html/source/autots.templates.html b/docs/build/html/source/autots.templates.html index 9a79e6fa..5f5a6e6b 100644 --- a/docs/build/html/source/autots.templates.html +++ b/docs/build/html/source/autots.templates.html @@ -14,10 +14,10 @@ gtag('config', 'G-P2KLF8302E'); - autots.templates package — AutoTS 0.6.11 documentation + autots.templates package — AutoTS 0.6.13 documentation - + diff --git a/docs/build/html/source/autots.tools.html b/docs/build/html/source/autots.tools.html index 24039fa1..3e826130 100644 --- a/docs/build/html/source/autots.tools.html +++ b/docs/build/html/source/autots.tools.html @@ -14,10 +14,10 @@ gtag('config', 'G-P2KLF8302E'); - autots.tools package — AutoTS 0.6.11 documentation + autots.tools package — AutoTS 0.6.13 documentation - + @@ -715,6 +715,11 @@

      Usage exampleautots.tools.fast_kalman.ensure_matrix(x, dim=1)¶

      +
      +
      +autots.tools.fast_kalman.ensure_stability(st)¶
      +
      +
      autots.tools.fast_kalman.holt_winters_damped_matrices(M, alpha, beta, gamma, phi=1.0)¶
      @@ -778,9 +783,19 @@

      Usage exampleautots.tools.fast_kalman.priv_update_with_nan_check(prior_mean, prior_covariance, observation_model, observation_noise, measurement, log_likelihood=False)¶

      +
      +
      +autots.tools.fast_kalman.random_matrix(rows, cols, density=0.2)¶
      +
      +
      -autots.tools.fast_kalman.random_state_space()¶
      +autots.tools.fast_kalman.random_state_space(tries=10)¶ +
      + +
      +
      +autots.tools.fast_kalman.random_state_space_original()¶

      Return randomly generated statespace models.

      @@ -809,6 +824,11 @@

      Usage example +
      +autots.tools.fast_kalman.stable_pinv(A, tol=1e-05, regularization=0.0001)¶
      +

      +
      autots.tools.fast_kalman.update(prior_mean, prior_covariance, observation_model, observation_noise, measurement)¶
      diff --git a/docs/build/html/source/intro.html b/docs/build/html/source/intro.html index 17eaa205..6f8fe0a4 100644 --- a/docs/build/html/source/intro.html +++ b/docs/build/html/source/intro.html @@ -14,10 +14,10 @@ gtag('config', 'G-P2KLF8302E'); - Intro — AutoTS 0.6.11 documentation + Intro — AutoTS 0.6.13 documentation - + @@ -47,7 +47,7 @@

      Intro¶

      AutoTS¶

      AutoTS is a time series package for Python designed for rapidly deploying high-accuracy forecasts at scale.

      -

      In 2023, AutoTS has won in the M6 forecasting competition, delivering the highest performance investment decisions across 12 months of stock market forecasting.

      +

      In 2023, AutoTS won in the M6 forecasting competition, delivering the highest performance investment decisions across 12 months of stock market forecasting.

      There are dozens of forecasting models usable in the sklearn style of .fit() and .predict(). These includes naive, statistical, machine learning, and deep learning models. Additionally, there are over 30 time series specific transforms usable in the sklearn style of .fit(), .transform() and .inverse_transform(). diff --git a/docs/build/html/source/modules.html b/docs/build/html/source/modules.html index 928b0921..2df63e72 100644 --- a/docs/build/html/source/modules.html +++ b/docs/build/html/source/modules.html @@ -14,10 +14,10 @@ gtag('config', 'G-P2KLF8302E'); - autots — AutoTS 0.6.11 documentation + autots — AutoTS 0.6.13 documentation - + diff --git a/docs/build/html/source/tutorial.html b/docs/build/html/source/tutorial.html index e52b7a31..478785ac 100644 --- a/docs/build/html/source/tutorial.html +++ b/docs/build/html/source/tutorial.html @@ -14,10 +14,10 @@ gtag('config', 'G-P2KLF8302E'); - Tutorial — AutoTS 0.6.11 documentation + Tutorial — AutoTS 0.6.13 documentation - + diff --git a/docs/conf.py b/docs/conf.py index 9ca3c7ca..fcece19f 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -25,7 +25,7 @@ # import AutoTS # from AutoTS import __version__ # release = __version__ -release = "0.6.12" +release = "0.6.13" # -- General configuration --------------------------------------------------- diff --git a/pyproject.toml b/pyproject.toml index 30432bc7..96994491 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta" [project] name = "autots" -version = "0.6.12" +version = "0.6.13" authors = [ { name="Colin Catlin", email="colin.catlin@gmail.com" }, ] diff --git a/setup.py b/setup.py index fb107b9a..38f6188b 100644 --- a/setup.py +++ b/setup.py @@ -32,7 +32,7 @@ setuptools.setup( name="AutoTS", - version="0.6.12", + version="0.6.13", author="Colin Catlin", author_email="colin.catlin@syllepsis.live", description="Automated Time Series Forecasting",

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