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Use MASE by default for PyAF Model Selection #229. Added a user datas…
…et. Model Not OK with MAPE, performs much better with MASE, RMSE., RMSSE
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# Thanks to https://colab.research.google.com/drive/1zaVQuobR8M63qB-UDDX8ZX37ctl98YIT | ||
# Marian W. : Predykcje niestety dość mocno mijają się z danymi historycznymi. | ||
# Translation from polish : Predictions, unfortunately, are quite far from historical data. | ||
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import pandas as pd | ||
import numpy as np | ||
import matplotlib.pyplot as plt | ||
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def prepare_dataset(): | ||
df = pd.read_csv("data/real-life/veturilo.csv", parse_dates=True, usecols=["ts","qnty"], index_col="ts" ) | ||
df.qnty.unique() | ||
df.qnty = df.qnty.replace("?", np.NaN).fillna(method='ffill').astype('uint8') | ||
df = df.qnty.resample("H").mean().to_frame() | ||
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df_predict = df.iloc[:] | ||
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df_tmp = df_predict.reset_index() | ||
df_tmp.columns = ['ds','y'] | ||
df_tmp.index = (0,) * len(df_tmp) | ||
df_tmp.index.name = 'unique_id' | ||
return df_tmp | ||
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import pyaf.ForecastEngine as autof | ||
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df = prepare_dataset() | ||
horizon = 48 | ||
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Y_train_df = df[:-horizon] | ||
Y_test_df = df[-horizon:] | ||
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lEngine = autof.cForecastEngine() | ||
lEngine.mOptions.mModelSelection_Criterion = "MAPE" | ||
lEngine.mOptions.mCycle_Criterion = "MAPE" | ||
lEngine.train(iInputDS = Y_train_df, iTime = 'ds', iSignal = 'y', iHorizon = horizon) | ||
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lEngine.getModelInfo() | ||
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print("\n\n<ModelInfo>") | ||
print(lEngine.to_json()); | ||
print("</ModelInfo>\n\n") | ||
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forecast_df= lEngine.forecast(Y_train_df, horizon) | ||
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print(forecast_df.head(horizon)) | ||
print(forecast_df.tail(horizon)) | ||
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lEngine.standardPlots("outputs/veturilo_MAPE") |
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# Thanks to https://colab.research.google.com/drive/1zaVQuobR8M63qB-UDDX8ZX37ctl98YIT | ||
# Marian W. : Predykcje niestety dość mocno mijają się z danymi historycznymi. | ||
# Translation from polish : Predictions, unfortunately, are quite far from historical data. | ||
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import pandas as pd | ||
import numpy as np | ||
import matplotlib.pyplot as plt | ||
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def prepare_dataset(): | ||
df = pd.read_csv("data/real-life/veturilo.csv", parse_dates=True, usecols=["ts","qnty"], index_col="ts" ) | ||
df.qnty.unique() | ||
df.qnty = df.qnty.replace("?", np.NaN).fillna(method='ffill').astype('uint8') | ||
df = df.qnty.resample("H").mean().to_frame() | ||
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df_predict = df.iloc[:] | ||
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df_tmp = df_predict.reset_index() | ||
df_tmp.columns = ['ds','y'] | ||
df_tmp.index = (0,) * len(df_tmp) | ||
df_tmp.index.name = 'unique_id' | ||
return df_tmp | ||
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import pyaf.ForecastEngine as autof | ||
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df = prepare_dataset() | ||
horizon = 48 | ||
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Y_train_df = df[:-horizon] | ||
Y_test_df = df[-horizon:] | ||
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lEngine = autof.cForecastEngine() | ||
lEngine.mOptions.mModelSelection_Criterion = "MASE" | ||
lEngine.mOptions.mCycle_Criterion = "MASE" | ||
lEngine.train(iInputDS = Y_train_df, iTime = 'ds', iSignal = 'y', iHorizon = horizon) | ||
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lEngine.getModelInfo() | ||
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print("\n\n<ModelInfo>") | ||
print(lEngine.to_json()); | ||
print("</ModelInfo>\n\n") | ||
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forecast_df= lEngine.forecast(Y_train_df, horizon) | ||
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print(forecast_df.head(horizon)) | ||
print(forecast_df.tail(horizon)) | ||
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lEngine.standardPlots("outputs/veturilo_MASE") |
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@@ -0,0 +1,48 @@ | ||
# Thanks to https://colab.research.google.com/drive/1zaVQuobR8M63qB-UDDX8ZX37ctl98YIT | ||
# Marian W. : Predykcje niestety dość mocno mijają się z danymi historycznymi. | ||
# Translation from polish : Predictions, unfortunately, are quite far from historical data. | ||
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import pandas as pd | ||
import numpy as np | ||
import matplotlib.pyplot as plt | ||
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def prepare_dataset(): | ||
df = pd.read_csv("data/real-life/veturilo.csv", parse_dates=True, usecols=["ts","qnty"], index_col="ts" ) | ||
df.qnty.unique() | ||
df.qnty = df.qnty.replace("?", np.NaN).fillna(method='ffill').astype('uint8') | ||
df = df.qnty.resample("H").mean().to_frame() | ||
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df_predict = df.iloc[:] | ||
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df_tmp = df_predict.reset_index() | ||
df_tmp.columns = ['ds','y'] | ||
df_tmp.index = (0,) * len(df_tmp) | ||
df_tmp.index.name = 'unique_id' | ||
return df_tmp | ||
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import pyaf.ForecastEngine as autof | ||
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df = prepare_dataset() | ||
horizon = 48 | ||
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Y_train_df = df[:-horizon] | ||
Y_test_df = df[-horizon:] | ||
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lEngine = autof.cForecastEngine() | ||
lEngine.mOptions.mModelSelection_Criterion = "RMSE" | ||
lEngine.mOptions.mCycle_Criterion = "RMSE" | ||
lEngine.train(iInputDS = Y_train_df, iTime = 'ds', iSignal = 'y', iHorizon = horizon) | ||
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lEngine.getModelInfo() | ||
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print("\n\n<ModelInfo>") | ||
print(lEngine.to_json()); | ||
print("</ModelInfo>\n\n") | ||
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forecast_df= lEngine.forecast(Y_train_df, horizon) | ||
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print(forecast_df.head(horizon)) | ||
print(forecast_df.tail(horizon)) | ||
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lEngine.standardPlots("outputs/veturilo_RMSE") |
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@@ -0,0 +1,48 @@ | ||
# Thanks to https://colab.research.google.com/drive/1zaVQuobR8M63qB-UDDX8ZX37ctl98YIT | ||
# Marian W. : Predykcje niestety dość mocno mijają się z danymi historycznymi. | ||
# Translation from polish : Predictions, unfortunately, are quite far from historical data. | ||
|
||
import pandas as pd | ||
import numpy as np | ||
import matplotlib.pyplot as plt | ||
|
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def prepare_dataset(): | ||
df = pd.read_csv("data/real-life/veturilo.csv", parse_dates=True, usecols=["ts","qnty"], index_col="ts" ) | ||
df.qnty.unique() | ||
df.qnty = df.qnty.replace("?", np.NaN).fillna(method='ffill').astype('uint8') | ||
df = df.qnty.resample("H").mean().to_frame() | ||
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df_predict = df.iloc[:] | ||
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df_tmp = df_predict.reset_index() | ||
df_tmp.columns = ['ds','y'] | ||
df_tmp.index = (0,) * len(df_tmp) | ||
df_tmp.index.name = 'unique_id' | ||
return df_tmp | ||
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import pyaf.ForecastEngine as autof | ||
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df = prepare_dataset() | ||
horizon = 48 | ||
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Y_train_df = df[:-horizon] | ||
Y_test_df = df[-horizon:] | ||
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lEngine = autof.cForecastEngine() | ||
lEngine.mOptions.mModelSelection_Criterion = "RMSSE" | ||
lEngine.mOptions.mCycle_Criterion = "RMSSE" | ||
lEngine.train(iInputDS = Y_train_df, iTime = 'ds', iSignal = 'y', iHorizon = horizon) | ||
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lEngine.getModelInfo() | ||
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print("\n\n<ModelInfo>") | ||
print(lEngine.to_json()); | ||
print("</ModelInfo>\n\n") | ||
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forecast_df= lEngine.forecast(Y_train_df, horizon) | ||
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print(forecast_df.head(horizon)) | ||
print(forecast_df.tail(horizon)) | ||
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lEngine.standardPlots("outputs/veturilo_RMSSE") |