Comparison of SARIMA, Bagging Exponential Smoothing with STL Decomposition and Robust STL Decomposition for Forecasting Red Chili Production

Abstract

Time series analysis enables the identification of trends and patterns in data, allowing for the development of forecasting models that predict future values. One effective approach for forecasting seasonal time series data is the Seasonal Autoregressive Integrated Moving Average (SARIMA) method. Bagging Exponential Smoothing with STL Decomposition (BES-STL) is an ensemble machine learning method aimed at enhancing forecasting accuracy. STL Method, which stands for Seasonal-Trend decomposition using Loess, is utilized to decompose time series data into three components, namely trend, seasonal, and remainder components. In the remainder component, the process of bootstrap aggregation (bagging) with Moving Block Bootstrapping (MBB) is used to obtain synthetic data, followed by averaging the value by month from the entire series as the forecast results. A comparative analysis was conducted using the SARIMA, BES-STL, and BES-RSTL models. The optimal model, with the lowest MAPE and RMSE, is then implemented to predict national red chili production. The results indicate that the SARIMA(1,1,1)(0,1,1)12 model has the best performance with a MAPE of 7.06 and a RMSE of 95,473. The top-performing model is utilized to forecast data from January to December 2022. Additionally, the forecasted results are compared to the actual data, resulting in a highly precise MAPE of 5.39.