arima-forecaster

What problem does it solve?

This Skill enables reliable forecasting of time-series data by fitting ARIMA and SARIMA models, providing predictions and confidence intervals to support data-driven decisions.

Core Features & Use Cases

• Auto-ARIMA parameter tuning: automatically selects p, d, q and seasonal components to fit the data.
• Seasonal and non-seasonal forecasting: handles trend, seasonality, and irregular patterns for monthly, quarterly, or daily data.
• Diagnostics & Interpretability: generates residual diagnostics and interpretable coefficients to support statistical reporting.
• Use Case: forecast monthly product demand and quantify forecast uncertainty for inventory planning.

Quick Start

Install the required libraries (statsmodels, pmdarima, pandas, numpy, matplotlib) and provide a pandas Series with a DateTime index. Then call the arima_forecaster(series, seasonal=True, m=12, forecast_steps=12) to obtain a forecast and confidence intervals.