Forecasting Indonesian food price inflation with multivariate time series and machine learning approaches

Ahmad Alfarrel Ghazali; Rossi Passarella; Zaqqi Yamani

doi:10.62189/ci.1695546

Forecasting Indonesian food price inflation with multivariate time series and machine learning approaches

Abstract

Food price inflation is a critical economic indicator significantly impacting public welfare. Accurate forecasting is essential, particularly in dynamic economies like Indonesia. This study forecasts Indonesian food price inflation using a multivariate time series approach combined with machine learning models. We utilized monthly data from the World Bank (Jan 2008-Dec 2023 for training/internal evaluation; Jan 2024-Dec 2024 for independent testing), including food price inflation and market prices (Open, High, Low, Close). We implemented and compared Support Vector Regression (SVR), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BILSTM) models based on their Root Mean Square Error (RMSE). On the internal evaluation dataset, the SVR model demonstrated superior performance with an RMSE of 4.7990 compared to LSTM and BILSTM. When forecasting actual future data for 2024, the SVR model yielded an RMSE of 8.9238, indicating its capability in predicting unseen data. This study concludes that SVR is an effective model for forecasting Indonesian food price inflation, especially in scenarios with limited data and moderate complexity, providing valuable insights for strategic decision-making aimed at supporting economic stability and public welfare.

Keywords

Thanks

We want to express sincere appreciation to all individuals who have made valuable contributions to this research.

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Details

Primary Language

English

Subjects

Computing Applications in Social Sciences and Education , Data Engineering and Data Science , Artificial Intelligence (Other)

Journal Section

Research Article

Authors

Ahmad Alfarrel Ghazali
0009-0003-1756-5091
Indonesia

Rossi Passarella ^*
0000-0002-7243-0451
Indonesia

Zaqqi Yamani
0009-0000-5796-3674
Indonesia

Early Pub Date

December 21, 2025

Publication Date

December 21, 2025

Submission Date

May 8, 2025

Acceptance Date

December 17, 2025

Published in Issue

Year 2025 Volume: 5 Number: 2

DOI

https://doi.org/10.62189/ci.1695546

IZ

https://izlik.org/JA83SG83ZR

Cite

RIS / Bibtex

Vancouver

1.Ahmad Alfarrel Ghazali, Rossi Passarella, Zaqqi Yamani. Forecasting Indonesian food price inflation with multivariate time series and machine learning approaches. Computers and Informatics. 2025 Dec. 1;5(2):46-57. doi:10.62189/ci.1695546