sktime_lgbm.py

from sktime.forecasting.compose import make_reduction, TransformedTargetForecaster
from sktime.forecasting.model_selection import ExpandingWindowSplitter, ForecastingGridSearchCV
from sktime.performance_metrics.forecasting import MeanAbsolutePercentageError
import lightgbm as lgb

def create_forecaster():

    # creating forecaster with LightGBM
    regressor = lgb.LGBMRegressor()
    forecaster = make_reduction(regressor, window_length=5, strategy="recursive")

    return forecaster


def grid_serch_forecaster(train, test, forecaster, param_grid):

    # Grid search on window_length
    cv = ExpandingWindowSplitter(initial_window=int(len(train) * 0.7))
    gscv = ForecastingGridSearchCV(
        forecaster, strategy="refit", cv=cv, param_grid=param_grid, 
        scoring=MeanAbsolutePercentageError(symmetric=True)
    )
    gscv.fit(train)
    print(f"best params: {gscv.best_params_}")

    # forecasting
    fh = np.arange(len(test)) + 1
    y_pred = gscv.predict(fh=fh)
    mae, mape = plot_forecast(train, test, y_pred)

    return mae, mape


param_grid = {
    "window_length": [5, 10, 15, 20, 25, 30] # parameter set to be grid searched
}
forecaster = create_forecaster()
sun_lgb_mae, sun_lgb_mape = grid_serch_forecaster(
    sun_train, sun_test, forecaster, param_grid
)

@hiteshgupta2507 Your input series needs to be indexed with the right frequency. Possible frequencies can be seen in this link.

Thanks for your quick response. I have been following your online material but couldn't find anywhere  freq being explicitly being fed into input series. https://towardsdatascience.com/multi-step-time-series-forecasting-with-arima-lightgbm-and-prophet-cc9e3f95dfb0 I was looking at the code nile data set code. The way I have my input data is where freq should be auto detected, right? df = pd.read_csv('uniques_pv_imps.csv', index_col = [0], parse_dates = True) On Monday, 20 September, 2021, 08:54:24 pm GMT-4, Tomonori Masui ***@***.***> wrote: @tomonori-masui commented on this gist. @hiteshgupta2507 Your input series needs to be indexed with the right frequency. Possible frequencies can be seen in this link. — You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub, or unsubscribe. Triage notifications on the go with GitHub Mobile for iOS or Android.

@hiteshgupta2507

I have been following your online material but couldn't find anywhere freq being explicitly being fed into input series.

Only the WPI data have that conversion in that blog post.

I was looking at the code nile data set code.

Nile dataset is not indexed with datetime values. It just has numeric values of years, hence it does not require frequency on its index.