Comparative Analysis of ARIMA and Fourier Series Methods for Air Temperature Forecasting in Surabaya - Salsabiila, Annas Thasya Haafizhah; Permata, Regita Putri; Hidayati, Sri (International Journal of Advances in Data and Information Systems, 2025)
Urban climate change, particularly rising temperatures and the Urban Heat Island (UHI) phenomenon, poses challenges for cities like Surabaya, Indonesia. This study compares the forecasting performance of ARIMA and ARIMA-Fourier models using daily air temperature data from 2020 to 2024. The analysis involved stationarity testing, model estimation, and evaluation across four forecasting horizons. ARIMA models (especially ARIMA(0,1,1) and ARIMA(1,1,0)) showed reliable short-term forecasts, but were less effective in capturing seasonal patterns. To address this, Fourier terms were integrated into the ARIMA framework. The ARIMA-Fourier model achieved better accuracy and higher R² values in short- and medium-term forecasts, particularly with an oscillation parameter of k = 150. However, its performance declined in long-term predictions due to overfitting risks. Overall, the ARIMA-Fourier model is more adaptive for capturing complex temperature seasonality and can support more accurate urban climate forecasting in Surabaya.