Temperature Forecast at Djuanda International Airport using ARIMA, ANN, and Hybrid ARIMA-ANN

Abstract

This research evaluates the performance of Artificial Neural Network (ANN) models in forecasting temperature at Djuanda Airport, comparing them with the traditional Autoregressive Integrated Moving Average (ARIMA) model and a hybrid ARIMA–ANN approach. Although statistical models such as ARIMA are widely applied, their capacity to capture nonlinear dynamics in tropical climate conditions is limited, particularly when the data exhibit irregular fluctuations that linear models cannot adequately represent. Forecasting temperatures in tropical airport settings, which is crucial for flight planning, operational safety, and the reliability of aviation operations, remains relatively underexplored. This gap underscores the importance of alternative modeling techniques that can effectively address nonlinear relationships. Using one year of observed data, the models are evaluated with three accuracy metrics: Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE). The ANN model achieves the lowest error values (MAE 0.7630, MAPE 2.7067%, RMSE 1.0074) compared to both ARIMA and hybrid approaches. The metrics and the testing graph collectively indicate that ANN has a stronger ability to capture nonlinear temperature dynamics in tropical contexts. Nonetheless, the findings must be interpreted with caution due to the limited dataset and single case study. These limitations highlight the need for extended data and alternative architectures to improve forecasting accuracy and strengthen support for safer aviation operations.