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All Journal Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi
Ali, Nur Anggraini T.
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Computer Science & IT Mathematics

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Pendekatan Hybrid VARIMA–ANN untuk Peramalan Multivariat Data Cuaca Bulanan di Provinsi Gorontalo Ali, Nur Anggraini T.; Wungguli, Djihad; Hasan, Isran K.
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 14 Issue 1 April 2026
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v14i1.37513

Abstract

Multivariate time series forecasting is essential for understanding the interrelationships among weather parameters. This study aims to develop a multivariate forecasting model using a hybrid Vector Autoregressive Integrated Moving Average (VARIMA)–Artificial Neural Network (ANN) approach with the backpropagation algorithm, applied to weather data from Gorontalo Province over the 2015–2023 period, including air temperature, humidity, and wind speed. The data were divided into training data (2015–2021) and testing data (2022–2023). The VARIMA model was employed to capture the linear component, while the residuals from the VARIMA model were subsequently modeled using ANN to capture nonlinear patterns. The order of the VARIMA model was determined based on the smallest Akaike Information Criterion (AIC) value, while model performance was evaluated using Mean Absolute Percentage Error (MAPE). The results indicate that the best-performing model is VARIMA(5,1,1)–ANN(18,9,3), with MAPE values of 1.32% for air temperature, 20.54% for humidity, and 21.96% for wind speed. These findings suggest that the hybrid VARIMA–ANN approach provides good forecasting performance and has the potential to serve as an alternative method for multivariate weather forecasting.   
Co-Authors Djihad Wungguli Isran K Hasan