text: #!/usr/bin/env python3 -u
  # -*- coding: utf-8 -*-
  __author__ = ["Markus Löning"]
  __all__ = [
      "ForecastingHorizon",
      "load_lynx",
      "load_longley",
      "load_airline",
      "load_shampoo_sales",
      "CutoffSplitter",
      "ForecastingGridSearchCV",
      "ForecastingRandomizedSearchCV",
      "SlidingWindowSplitter",
      "SingleWindowSplitter",
      "ExpandingWindowSplitter",
      "temporal_train_test_split",
      "NaiveForecaster",
      "ExponentialSmoothing",
      "ThetaForecaster",
      "AutoARIMA",
      "ARIMA",
      "AutoETS",
      "Prophet",
      "PolynomialTrendForecaster",
      "TransformedTargetForecaster",
      "MultiplexForecaster",
      "Deseasonalizer",
      "ReducedForecaster",
      "EnsembleForecaster",
      "Detrender",
      "pd",
      "np",
      "plot_series",
      "NormalHedgeEnsemble",
      "NNLSEnsemble",
      "OnlineEnsembleForecaster",
      "evaluate",
      "make_forecasting_scorer",
      "MeanAbsoluteScaledError",
      "MedianAbsoluteScaledError",
      "MeanSquaredScaledError",
      "MedianSquaredScaledError",
      "MeanAbsoluteError",
      "MeanSquaredError",
      "MedianAbsoluteError",
      "MedianSquaredError",
      "MeanAbsolutePercentageError",
      "MedianAbsolutePercentageError",
      "MeanSquaredPercentageError",
      "MedianSquaredPercentageError",
      "MeanRelativeAbsoluteError",
      "MedianRelativeAbsoluteError",
      "GeometricMeanRelativeAbsoluteError",
      "GeometricMeanRelativeSquaredError",
      "MeanAsymmetricError",
      "RelativeLoss",
      "mean_absolute_scaled_error",
      "median_absolute_scaled_error",
      "mean_squared_scaled_error",
      "median_squared_scaled_error",
      "mean_absolute_error",
      "mean_squared_error",
      "median_absolute_error",
      "median_squared_error",
      "mean_absolute_percentage_error",
      "median_absolute_percentage_error",
      "mean_squared_percentage_error",
      "median_squared_percentage_error",
      "mean_relative_absolute_error",
      "median_relative_absolute_error",
      "geometric_mean_relative_absolute_error",
      "geometric_mean_relative_squared_error",
      "relative_loss",
      "mean_asymmetric_error",
  ]
  
  import numpy as np
  import pandas as pd
  
  from sktime.datasets import load_airline
  from sktime.datasets import load_longley
  from sktime.datasets import load_lynx
  from sktime.datasets import load_shampoo_sales
  from sktime.forecasting.base import ForecastingHorizon
  from sktime.forecasting.theta import ThetaForecaster
  from sktime.forecasting.trend import PolynomialTrendForecaster
  from sktime.forecasting.arima import AutoARIMA
  from sktime.forecasting.arima import ARIMA
  from sktime.forecasting.fbprophet import Prophet
  from sktime.forecasting.ets import AutoETS
  from sktime.forecasting.compose import EnsembleForecaster
  from sktime.forecasting.compose import ReducedForecaster
  from sktime.forecasting.compose import TransformedTargetForecaster
  from sktime.forecasting.compose import MultiplexForecaster
  from sktime.forecasting.exp_smoothing import ExponentialSmoothing
  from sktime.forecasting.model_selection import CutoffSplitter
  from sktime.forecasting.model_selection import ExpandingWindowSplitter
  from sktime.forecasting.model_selection import ForecastingGridSearchCV
  from sktime.forecasting.model_selection import ForecastingRandomizedSearchCV
  from sktime.forecasting.model_selection import SingleWindowSplitter
  from sktime.forecasting.model_selection import SlidingWindowSplitter
  from sktime.forecasting.model_selection import temporal_train_test_split
  from sktime.forecasting.model_evaluation import evaluate
  from sktime.forecasting.naive import NaiveForecaster
  from sktime.forecasting.online_learning._online_ensemble import (
      OnlineEnsembleForecaster,
  )
  from sktime.forecasting.online_learning._prediction_weighted_ensembler import (
      NNLSEnsemble,
      NormalHedgeEnsemble,
  )
  from sktime.transformations.series.detrend import Deseasonalizer
  from sktime.transformations.series.detrend import Detrender
  from sktime.utils.plotting import plot_series
  from sktime.performance_metrics.forecasting import (
      make_forecasting_scorer,
      MeanAbsoluteScaledError,
      MedianAbsoluteScaledError,
      MeanSquaredScaledError,
      MedianSquaredScaledError,
      MeanAbsoluteError,
      MeanSquaredError,
      MedianAbsoluteError,
      MedianSquaredError,
      MeanAbsolutePercentageError,
      MedianAbsolutePercentageError,
      MeanSquaredPercentageError,
      MedianSquaredPercentageError,
      MeanRelativeAbsoluteError,
      MedianRelativeAbsoluteError,
      GeometricMeanRelativeAbsoluteError,
      GeometricMeanRelativeSquaredError,
      MeanAsymmetricError,
      RelativeLoss,
      mean_absolute_scaled_error,
      median_absolute_scaled_error,
      mean_squared_scaled_error,
      median_squared_scaled_error,
      mean_absolute_error,
      mean_squared_error,
      median_absolute_error,
      median_squared_error,
      mean_absolute_percentage_error,
      median_absolute_percentage_error,
      mean_squared_percentage_error,
      median_squared_percentage_error,
      mean_relative_absolute_error,
      median_relative_absolute_error,
      geometric_mean_relative_absolute_error,
      geometric_mean_relative_squared_error,
      mean_asymmetric_error,
      relative_loss,
  )
  