Evaluasi pendekatan sliding window terhadap static split dalam prediksi harga Bitcoin menggunakan algoritma Random Forest

Findra Kartika Sari Dewi; Herlina Herlina; Vito Carlen Giovanni

doi:10.24246/aiti.v23i1.152-162

Authors

Findra Kartika Sari Dewi Program Studi Informatika, Universitas Atma Jaya Yogyakarta https://orcid.org/0009-0000-4919-3235
Herlina Program Studi Informatika, Universitas Atma Jaya Yogyakarta
Vito Carlen Giovanni Program Studi Informatika, Universitas Atma Jaya Yogyakarta

DOI:

https://doi.org/10.24246/aiti.v23i1.152-162

Keywords:

Bitcoin, price prediction, Random Forest, rolling window, static split

Abstract

Bitcoin price prediction remains a complex and relevant challenge due to the asset’s high volatility and increasing adoption across sectors. One important factor influencing prediction performance is the data-splitting strategy used during model development. This study compares two approaches (sliding window and static split) in the context of Bitcoin price forecasting using the Random Forest algorithm. Historical data from Yahoo Finance spanning 2015 to 2022 is used, with input features constructed from closing prices and seven daily lags. The sliding window method trains the model with a moving 365-day window and tests it on the following day, whereas the static split uses a fixed date-based partition. Evaluation is conducted using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), R-squared (R²), Mean Absolute Percentage Error (MAPE), and direction accuracy metrics. The results indicate that the sliding window approach produces more accurate and consistent predictions and better captures directional trends in price movement than the static split method.

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References

S. Bistarelli, F. Santini, and L. M. Tutino, “A Short Survey on Bitcoin Price Prediction,” in CEUR Workshop Proceedings, Turin, Italy, May 2024. [Online]. Available: https://francescosantini.sites.dmi.unipg.it/

P. Jaquart, D. Dann, and C. Weinhardt, “Short-term bitcoin market prediction via machine learning,” Journal of Finance and Data Science, vol. 7, pp. 45–66, Nov. 2021, doi: 10.1016/j.jfds.2021.03.001. DOI: https://doi.org/10.1016/j.jfds.2021.03.001

J. Chen, “Analysis of Bitcoin Price Prediction Using Machine Learning,” Journal of Risk and Financial Management, vol. 16, no. 1, p. 51, Jan. 2023, doi: 10.3390/jrfm16010051. DOI: https://doi.org/10.3390/jrfm16010051

A. Dimitriadou and A. Gregoriou, “Predicting Bitcoin Prices Using Machine Learning,” Entropy, vol. 25, no. 5, May 2023, doi: 10.3390/e25050777. DOI: https://doi.org/10.3390/e25050777

R. P. Masini, M. C. Medeiros, and E. F. Mendes, “Machine Learning Advances for Time Series Forecasting,” J Econ Surv, vol. 37, no. 1, pp. 76–111, Dec. 2020, [Online]. Available: http://arxiv.org/abs/2012.12802 DOI: https://doi.org/10.1111/joes.12429

T. Awoke, M. Rout, L. Mohanty, and S. C. Satapathy, “Bitcoin Price Prediction and Analysis Using Deep Learning Models,” Lecture Notes in Networks and Systems, vol. 134, pp. 631–640, 2021, doi: 10.1007/978-981-15-5397-4_63. DOI: https://doi.org/10.1007/978-981-15-5397-4_63

J. Chevallier, D. Guégan, and S. Goutte, “Is It Possible to Forecast the Price of Bitcoin?,” Forecasting, vol. 3, no. 2, pp. 377–420, Jun. 2021, doi: 10.3390/forecast3020024. DOI: https://doi.org/10.3390/forecast3020024

H. Pabuçcu, S. Ongan, and A. Ongan, “Forecasting the movements of Bitcoin prices: an application of machine learning algorithms,” Quantitative Finance and Economics, vol. 4, no. 4, pp. 679–692, 2020, doi: 10.3934/QFE.2020031. DOI: https://doi.org/10.3934/QFE.2020031

A. Ibrahim, R. Kashef, and L. Corrigan, “Predicting market movement direction for bitcoin: A comparison of time series modeling methods,” Computers and Electrical Engineering, vol. 89, Jan. 2021, doi: 10.1016/j.compeleceng.2020.106905. DOI: https://doi.org/10.1016/j.compeleceng.2020.106905

R. Chiong, Z. Fan, Z. Hu, and S. Dhakal, “A Novel Ensemble Learning Approach for Stock Market Prediction Based on Sentiment Analysis and the Sliding Window Method,” IEEE Trans Comput Soc Syst, vol. 10, no. 5, pp. 2613–2623, Oct. 2023, doi: 10.1109/TCSS.2022.3182375. DOI: https://doi.org/10.1109/TCSS.2022.3182375

I. H. Shakri, “Time series prediction using machine learning: a case of Bitcoin returns,” Studies in Economics and Finance, vol. 39, no. 3, pp. 458–470, Apr. 2022, doi: 10.1108/SEF-06-2021-0217. DOI: https://doi.org/10.1108/SEF-06-2021-0217

S. Javed Parvez and S. C. Dhanush, “Bitcoin price prediction using Random Forest Regression,” Journal of Positive School Psychology, vol. 6, no. 4, pp. 4352–4358, 2022, [Online]. Available: http://journalppw.com

T. Singh, S. Mishra, D. Pandey, C. Sharma, S. Koli, and K. Joshi, “Crypto currency-bitcoin Price Predictor using Linear Regression and Random Forest,” in 2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024, Institute of Electrical and Electronics Engineers Inc., 2024. doi: 10.1109/ICEECT61758.2024.10738906. DOI: https://doi.org/10.1109/ICEECT61758.2024.10738906

M. Ula, V. Ilhadi, and Z. M. Sidek, “Comparing Long Short-Term Memory and Random Forest Accuracy for Bitcoin Price Forecasting,” MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 23, no. 2, pp. 259–272, Jan. 2024, doi: 10.30812/matrik.v23i2.3267. DOI: https://doi.org/10.30812/matrik.v23i2.3267

S. Saadah and H. Salsabila, “Prediksi Harga Bitcoin Menggunakan Metode Random Forest (Studi Kasus: Data Acak Pada Awal Masa Pandemic Covid-19),” Jurnal Komputer Terapan, vol. 7, no. 1, pp. 24–32, 2021, [Online]. Available: https://jurnal.pcr.ac.id/index.php/jkt/ DOI: https://doi.org/10.35143/jkt.v7i1.4618

A. Ho, R. Vatambeti, and S. K. Ravichandran, “Bitcoin Price Prediction Using Machine Learning and Artificial Neural Network Model,” Indian J Sci Technol, vol. 14, no. 27, pp. 2300–2308, Jul. 2021, doi: 10.17485/IJST/v14i27.878. DOI: https://doi.org/10.17485/IJST/v14i27.878

V. Derbentsev, A. Matviychuk, and V. N. Soloviev, “Forecasting of Cryptocurrency Prices Using Machine Learning,” in Advanced Studies of Financial Technologies and Cryptocurrency Markets, Springer Singapore, 2020, pp. 211–231. doi: 10.1007/978-981-15-4498-9_12. DOI: https://doi.org/10.1007/978-981-15-4498-9_12

I. Reis, D. Baron, and S. Shahaf, “Probabilistic Random Forest: A Machine Learning Algorithm for Noisy Data Sets,” Astron J, vol. 157, no. 1, p. 16, Jan. 2019, doi: 10.3847/1538-3881/aaf101. DOI: https://doi.org/10.3847/1538-3881/aaf101

B. Goehry, H. Yan, Y. Goude, P. Massart, and J. M. Poggi, “Random Forests for Time Series,” REVSTAT-Statistical Journal, vol. 21, no. 2, pp. 283–302, Jun. 2023, doi: 10.57805/revstat.v21i2.400.