forecast.py

import numpy as np
import pandas as pd
from sktime.forecasting.compose import ReducedRegressionForecaster
from sktime.forecasting.model_selection import temporal_train_test_split
from sktime.performance_metrics.forecasting import smape_loss
from sklearn.linear_model import LinearRegression
from sklearn.neighbors import KNeighborsRegressor
from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor, ExtraTreesRegressor
from sklearn.svm import LinearSVR
from xgboost import XGBRegressor


def select_regressor(selection):
    regressors = {
        'Linear Regression': LinearRegression(),
        'K-Nearest Neighbors': KNeighborsRegressor(),
        'Random Forest': RandomForestRegressor(),
        'Gradient Boosting': GradientBoostingRegressor(),
        'XGBoost': XGBRegressor(verbosity=0),
        'Support Vector Machines': LinearSVR(),
        'Extra Trees': ExtraTreesRegressor(),
    }

    return regressors[selection]


def generate_forecast(df_, regressor, forecast_horizon, window_length):
    df = df_.copy()
    # Replacing NaN values with the forward fill method
    df.fillna(method='ffill', inplace=True)

    # Resetting the index of the time series,
    # because sktime doesn't support DatetimeIndex for now
    y_train = df.iloc[:, 0].reset_index(drop=True)
    fh = np.arange(forecast_horizon) + 1
    regressor = select_regressor(regressor)
    forecaster = ReducedRegressionForecaster(regressor=regressor, window_length=window_length,
                                             strategy='recursive')
    forecaster.fit(y_train, fh=fh)
    y_pred = forecaster.predict(fh)

    date = df.index[0]
    periods = df.shape[0] + forecast_horizon
    # Creating a new DatetimeIndex that goes
    # as far in the future as the forecast horizon
    date_index = pd.date_range(date, periods=periods, freq='H')

    col_name = df.columns[0] + ' Forecast'
    df_pred = pd.DataFrame({col_name: y_pred})
    # Appending the forecast as a new column to the dataframe
    df = df.append(df_pred, ignore_index=True)
    # Setting the DatetimeIndex we created
    # as the new index of the dataframe
    df.set_index(date_index, inplace=True)

    return df


def calculate_smape(df_, regressor, forecast_horizon, window_length):
    df = df_.copy()
    df.fillna(method='ffill', inplace=True)
    y = df.iloc[:, 0].reset_index(drop=True)
    y_train, y_test = temporal_train_test_split(y, test_size=forecast_horizon)
    fh = np.arange(y_test.shape[0]) + 1
    regressor = select_regressor(regressor)
    forecaster = ReducedRegressionForecaster(regressor=regressor, window_length=window_length,
                                             strategy='recursive')
    forecaster.fit(y_train, fh=fh)
    y_pred = forecaster.predict(fh)

    return smape_loss(y_pred, y_test)