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Vol.15, No.3, August 2026. ISSN: 2217-8309 eISSN: 2217-8333
TEM Journal
TECHNOLOGY, EDUCATION, MANAGEMENT, INFORMATICS Association for Information Communication Technology Education and Science |
Time Series Forecasting with Ensemble Methods for Inflation in Indonesia
Eni Sumarminingsih, Rahma Fitriani, Aqsa Yudhistira Redi, Satriadi Putra Santika, Muhammad Fahmi Fauzan, Natasha Aulia
© 2026 Eni Sumarminingsih, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)
Citation Information: TEM Journal. Volume 15, Issue 3, Pages 2427-2438, ISSN 2217-8309, DOI: 10.18421/TEM153-33, August 2026.
Received: 25 March 2025.
Abstract:
Inflation is an economic problem experienced by almost all countries in the world so inflation needs to be controlled. Controlling inflation requires accurate inflation forecasting. In this study, the ensemble method was used for the modeling. Three ensemble methods were tested, namely simple average, trimmed average and variance-based. The individual models applied in this research are based on Machine Learning, which include Long Short-Term Memory (LSTM), Extreme Learning Machine (ELM), and Feed Forward Neural Network (FFNN). In selecting inputs for individual models, statistical tools were used, namely correlation analysis and partial autocorrelation function. Among the three individual models, the best model was the FFNN model. While the best ensemble method was variance-based. The ensemble method can reduce the RMSE of the best individual model.
Keywords – Ensemble Time Series, Indonesian inflation, Long Short-Term Memory, Extreme Learning Machine, Feed Forward Neural Network. |
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