testing sktime

v2/test_predictor_sktime.py

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+import pandas as pd
+import matplotlib.pyplot as plt
+
+from sktime.datasets import load_airline
+from sktime.forecasting.base import ForecastingHorizon
+from sktime.forecasting.naive import NaiveForecaster
+from sktime.forecasting.arima import AutoARIMA
+from sktime.forecasting.ets import AutoETS
+
+from sktime.performance_metrics.forecasting import MeanAbsolutePercentageError
+from sktime.split import temporal_train_test_split
+from sktime.split import ExpandingWindowSplitter
+from sktime.forecasting.model_evaluation import evaluate
+from sktime.utils.plotting import plot_series
+
+
+# step 1: splitting historical data
+y = load_airline()
+
+# forecaster = NaiveForecaster(strategy="last", sp=12)
+forecaster = AutoETS(auto=True, sp=12, n_jobs=-1)
+# forecaster = AutoARIMA(sp=12, suppress_warnings=True)
+
+cv = ExpandingWindowSplitter(
+    step_length=12, fh=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], initial_window=72
+)
+
+df = evaluate(forecaster=forecaster, y=y, cv=cv, strategy="refit", return_data=True)
+
+print(df['y_pred'])
+
+fig, ax = plot_series(
+    y,
+    df["y_pred"].iloc[0],
+    df["y_pred"].iloc[1],
+    df["y_pred"].iloc[2],
+    df["y_pred"].iloc[3],
+    df["y_pred"].iloc[4],
+    df["y_pred"].iloc[5],
+    markers=["o", "", "", "", "", "", ""],
+    labels=["y_true"] + ["y_pred (Backtest " + str(x) + ")" for x in range(6)],
+)
+ax.legend()
+plt.show()
+
+
+
+y_train, y_test = temporal_train_test_split(y, test_size=len(y.index) // 2)
+
+# step 2: running the basic forecasting workflow
+fh = ForecastingHorizon(y_test.index, is_relative=False)
+
+
+
+forecaster.fit(y_train)
+y_pred = forecaster.predict(fh)
+
+plot_series(y_train, y_test, y_pred, labels=["y_train", "y_test", "y_pred"])
+plt.show()
+
+# step 3: specifying the evaluation metric
+mape = MeanAbsolutePercentageError(symmetric=False)
+# if function interface is used, just use the function directly in step 4
+
+# step 4: computing the forecast performance
+print(mape(y_test, y_pred))