Random Forest Regressor to predict Time Series

rf_timeseries.py

# Fitting Random Forest Regression to the dataset
# To import sklearn's regressor
from sklearn.ensemble import RandomForestRegressor

# create regressor object
regressor = RandomForestRegressor(n_estimators = 100, random_state = 0)

# fit the regressor with x and y data
regressor.fit(train_X, train_y)

# df contains transformed feature data for 2020Q4
y_pred = regressor.predict(df.values)
predictions = [value for value in y_pred]

# evaluate model using regression values
actual_humidity = output_2020['humidity'].values
mean_squared_error(actual_humidity, predictions)