Forecasting the Value of Indonesia’s Exports using Model Hybrid Arimax-NN - Faradilla Putri Wahidah; Sri Wahyuningsih; Meiliyani Siringoringo (Science & Technology Asia, 2024)
Time sequence data often exhibits both linear and nonlinear patterns, which can lead to inaccurate forecasts when using traditional methods that are limited to capturing only one type of pattern. To address this limitation, this study employs a hybrid method that combines the strengths of Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) and neural networks (NN). The ARIMAX model effectively captures linear patterns, while the NN excels at modeling nonlinearities. The primary objective of this research is to optimize the ARIMAX-NN hybrid model for forecasting Indonesia’s export values. Through rigorous model selection, the ARIMAX ([1,5,12],1,0)-NN 1 neuron model emerged as the best-performing configuration, achieving the lowest Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and Mean Absolute Deviation (MAD) values. The forecasts for January December 2024 reveal a pattern of decreasing export values during the month of Eid al-Fitr, a trend consistent with historical patterns and economic insights.