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| from gtime import * | |
| from gtime.feature_extraction import * | |
| import pandas as pd | |
| import numpy as np | |
| from sklearn.linear_model import LinearRegression | |
| # Create random DataFrame with DatetimeIndex | |
| X_dt = pd.DataFrame(np.random.randint(4, size=(20)), | |
| index=pd.date_range("2019-12-20", "2020-01-08"), | |
| columns=['time_series']) | |
| # Convert the DatetimeIndex to PeriodIndex and create y matrix | |
| X = preprocessing.TimeSeriesPreparation().transform(X_dt) | |
| y = model_selection.horizon_shift(X, horizon=2) | |
| # Create some features | |
| cal = feature_generation.Calendar(region="europe", country="Switzerland", kernel=np.array([1, 2])) | |
| X_f = compose.FeatureCreation( | |
| [('s_2', Shift(2), ['time_series']), | |
| ('ma_3', MovingAverage(window_size=3), ['time_series']), | |
| ('cal', cal, ['time_series'])]).fit_transform(X) | |
| # Train/test split | |
| X_train, y_train, X_test, y_test = model_selection.FeatureSplitter().transform(X_f, y) | |
| # Try some different forecasting models (TrendForecaster doesn't need computed features) | |
| gar = forecasting.GAR(LinearRegression()) | |
| gar.fit(X_train, y_train).predict(X_test) | |
| X_train, _, X_test, _ = model_selection.FeatureSplitter().transform(X, y) | |
| tf = forecasting.TrendForecaster(trend='polynomial', trend_x0=np.zeros(3)) | |
| tf.fit(X_train).predict(X_test) |
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