CRYPTOCURRENCY TIME SERIES FORECASTING MODEL USING GRU ALGORITHM BASED ON MACHINE LEARNING

  • Melina Melina Department of Informatics, Faculty of Science and Informatics, Universitas Jenderal Achmad Yani, Indonesia https://orcid.org/0000-0002-2180-613X
  • Sukono Sukono Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Indonesia https://orcid.org/0000-0003-2608-9712
  • Herlina Napitupulu Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Indonesia https://orcid.org/0000-0002-1638-282X
  • Norizan Mohamed Department of Mathematics, Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia, Malaysia https://orcid.org/0000-0001-9058-6153
  • Yulison Herry Chrisnanto Department of Informatics, Faculty of Science and Informatics, Universitas Jenderal Achmad Yani, Indonesia https://orcid.org/0000-0002-3491-3049
  • Asep ID Hadiana Department of Informatics, Faculty of Science and Informatics, Universitas Jenderal Achmad Yani, Indonesia https://orcid.org/0000-0002-4372-6165
  • Valentina Adimurti Kusumaningtyas Department of Chemistry, Faculty of Science and Informatics, Universitas Jenderal Achmad Yani, Indonesia https://orcid.org/0000-0001-9552-7048
DOI: https://doi.org/10.30598/barekengvol19iss2pp1317-1328
Keywords: Cryptocurrency, GRU, Learning Curve, Machine Learning, Time Series

Abstract

The cryptocurrency market is experiencing rapid growth in the world. The high fluctuation and volatility of cryptocurrency prices and the complexity of non-linear relationships in data patterns attract investors and researchers who want to develop accurate cryptocurrency price forecasting models. This research aims to build a cryptocurrency forecasting model with a machine learning-based time series approach using the gated recurrent units (GRU) algorithm. The dataset used is historical Bitcoin closing price data from January 1, 2017, to July 31, 2024. Based on the gap in previous research, the selected model is only based on the accuracy value. In this study, the chosen model must fulfill two criteria: the best-fitting model based on the learning curve diagnosis and the model with the best accuracy value. The selected model is used to forecast the test data. Model selection with these two criteria has resulted in high accuracy in model performance. This research was highly accurate for all tested models with MAPE < 10%. The GRU 30-50 model is best tested with MAE = 867.2598, RMSE = 1330.427, and MAPE = 1.95%. Applying the sliding window technique makes the model accurate and fast in learning the pattern of time series data, resulting in a best-fitting model based on the learning curve diagnosis.

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Published
2025-04-01
How to Cite
[1]
M. Melina, “CRYPTOCURRENCY TIME SERIES FORECASTING MODEL USING GRU ALGORITHM BASED ON MACHINE LEARNING”, BAREKENG: J. Math. & App., vol. 19, no. 2, pp. 1317-1328, Apr. 2025.
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Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
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Articles

Copyright (c) 2025 Melina Melina, Sukono Sukono, Herlina Napitupulu, Norizan Mohamed, Yulison Herry Chrisnanto, Asep ID Hadiana, Valentina Adimurti Kusumaningtyas

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