Predicting Rainfall Patterns in Palakkad District of South India Based on Time SeriesForecasting Approaches

Abstract

Rainfall prediction is inevitable for managing agricultural activities, water resources, and mitigating the risks of droughts and floods,
particularly in regions like the Palakkad district, Kerala, which is known for its agricultural significance. Time series forecasting
plays a vital role in identifying patterns, trends, and seasonal variations in rainfall data. In this study the Holt-Winters exponential
smoothing model and the Seasonal Autoregressive Integrated Moving Average (SARIMA) model are considered for predicting the
rainfall in Palakkad district, Kerala. The forecast accuracy metrics Root Mean Squared Error (RMSE) and Mean Absolute Percent
Error (MAPE) are utilised to evaluate the performance of the above models. While the Holt-Winters exponential smoothing model
effectively captured simple seasonal patterns, the Seasonal Autoregressive Integrated Moving Average (SARIMA) model excelled
in handling complex seasonal structures and trends. The Seasonal Autoregressive Integrated Moving Average (SARIMA) model
was identified as the most accurate, with minimal forecast errors, adherence to assumptions, and low residual correlations.