Deep learning-based short-term cryptocurrency price forecasting using LSTM models enhanced with technical indicators

Yusuf Çelik; İrem Sevda İnce

doi:10.29132/ijpas.1906671

Deep learning-based short-term cryptocurrency price forecasting using LSTM models enhanced with technical indicators

Abstract

This study investigates short-term cryptocurrency price forecasting using deep learning methods. Minute-level price and volume data for BTC/USDT and ETH/USDT trading pairs were obtained through the Binance API and analyzed under different temporal resolutions, including hourly and 15-minute forecasting scenarios. Recurrent neural network architectures, including LSTM, GRU, and BiLSTM, were evaluated using different loss functions to identify effective configurations for short-term forecasting. Experimental results show that deep learning models can achieve high forecasting accuracy for high-frequency cryptocurrency data. In particular, the LSTM model trained with a weighted loss function demonstrated strong and stable prediction performance. In addition, incorporating technical indicators as auxiliary features enriched the feature space and improved forecasting capability. The results also suggest that aligning the temporal resolution of technical indicators with the prediction frequency can further enhance model performance. Overall, the proposed framework provides an effective deep learning–based approach for short-term cryptocurrency price forecasting in high-frequency financial markets.

Keywords

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Details

Primary Language

English

Subjects

Pattern Recognition

Journal Section

Research Article

Authors

Yusuf Çelik ^*
0000-0002-7859-7543
Türkiye

İrem Sevda İnce
0009-0007-0723-7042
Türkiye

Publication Date

June 22, 2026

Submission Date

March 10, 2026

Acceptance Date

May 15, 2026

Published in Issue

Year 2026 Volume: 12 Number: 1

DOI

https://doi.org/10.29132/ijpas.1906671

IZ

https://izlik.org/JA63HY25DP

Cite

RIS / Bibtex

APA

Çelik, Y., & İnce, İ. S. (2026). Deep learning-based short-term cryptocurrency price forecasting using LSTM models enhanced with technical indicators. International Journal of Pure and Applied Sciences, 12(1), 316-334. https://doi.org/10.29132/ijpas.1906671

AMA

1.Çelik Y, İnce İS. Deep learning-based short-term cryptocurrency price forecasting using LSTM models enhanced with technical indicators. International Journal of Pure and Applied Sciences. 2026;12(1):316-334. doi:10.29132/ijpas.1906671

Chicago

Çelik, Yusuf, and İrem Sevda İnce. 2026. “Deep Learning-Based Short-Term Cryptocurrency Price Forecasting Using LSTM Models Enhanced With Technical Indicators”. International Journal of Pure and Applied Sciences 12 (1): 316-34. https://doi.org/10.29132/ijpas.1906671.

EndNote

Çelik Y, İnce İS (June 1, 2026) Deep learning-based short-term cryptocurrency price forecasting using LSTM models enhanced with technical indicators. International Journal of Pure and Applied Sciences 12 1 316–334.

IEEE

[1]Y. Çelik and İ. S. İnce, “Deep learning-based short-term cryptocurrency price forecasting using LSTM models enhanced with technical indicators”, International Journal of Pure and Applied Sciences, vol. 12, no. 1, pp. 316–334, June 2026, doi: 10.29132/ijpas.1906671.

ISNAD

Çelik, Yusuf - İnce, İrem Sevda. “Deep Learning-Based Short-Term Cryptocurrency Price Forecasting Using LSTM Models Enhanced With Technical Indicators”. International Journal of Pure and Applied Sciences 12/1 (June 1, 2026): 316-334. https://doi.org/10.29132/ijpas.1906671.

JAMA

1.Çelik Y, İnce İS. Deep learning-based short-term cryptocurrency price forecasting using LSTM models enhanced with technical indicators. International Journal of Pure and Applied Sciences. 2026;12:316–334.

MLA

Çelik, Yusuf, and İrem Sevda İnce. “Deep Learning-Based Short-Term Cryptocurrency Price Forecasting Using LSTM Models Enhanced With Technical Indicators”. International Journal of Pure and Applied Sciences, vol. 12, no. 1, June 2026, pp. 316-34, doi:10.29132/ijpas.1906671.

Vancouver

1.Yusuf Çelik, İrem Sevda İnce. Deep learning-based short-term cryptocurrency price forecasting using LSTM models enhanced with technical indicators. International Journal of Pure and Applied Sciences. 2026 Jun. 1;12(1):316-34. doi:10.29132/ijpas.1906671