Enhancing Agricultural Commodity Forecasting: A Median-Based Combination of Time Series Models

Abstract

This study explores the efficacy of combining forecasts using the median operator to enhance forecasting performance. The traditional approach of assigning equal weights to individual models often struggles with extreme forecasts. A new method Simple Combination of Univariate Models (SCUM) is utilized, which uses the median operator to combine forecasts from four distinct time series models: Exponential Smoothing (ETS), Auto Regressive Integrated Moving Average (ARIMA), Dynamically Optimised Theta Model (DOTM), and Complex Exponential Smoothing (CES). This approach aims to mitigate the influence of extreme forecasts and improve overall accuracy. Our empirical analysis investigates the use of the SCUM approach for the agricultural commodity data. Yearly production of Rice is used, sourced from the Ministry of Agriculture & Farmers Welfare, Government of India. The forecasting performance of the SCUM approach is compared against individual models and a mean-based combined forecast using key performance metrics such as Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and Relative Efficiency. The results show that SCUM outperforms all other models, achieving the lowest RMSE (51.89) and a MAPE of 6.90, indicating superior in-sample forecasting accuracy. ARIMA was the least efficient with a 22.11% relative efficiency, while the Mean combination method (3.33%) was closest to SCUM in performance. The findings suggest that SCUM is not only a viable alternative to traditional methods but also offers significant advantages in improving forecasting accuracy. This research underscores the potential for median-based forecast combinations to achieve superior predictive accuracy and reliability, making it a valuable tool for scholars and practitioners in time series forecasting.