arima-forecaster

Time-series forecasting with seasonality is handled by fitting SARIMA models to your data. This Skill automates parameter selection using pmdarima and statsmodels, generating future predictions and confidence intervals from a pandas Series with a DateTime index.

Automatically tuning ARIMA parameters is best achieved using the auto_arima function from the pmdarima library. This Skill leverages auto_arima to automatically select the optimal p, d, q and seasonal components to fit your specific time-series data.

Yes, you can use pandas and numpy to prepare data for ARIMA forecasting. The Skill requires a pandas Series with a DateTime index as input, while numpy supports the underlying numerical operations for the time-series forecasting models.

Time-series forecasting with SARIMA does provide confidence intervals for demand planning. The Skill outputs a forecast series alongside quantified confidence intervals, enabling you to measure forecast uncertainty for inventory planning and data-driven decisions.

Residual diagnostics are needed after fitting an ARIMA model to validate the statistical assumptions of your forecast. This Skill generates residual diagnostics and interpretable coefficients to support statistical reporting and ensure model reliability.