About this article
- Submitted: Jan 12, 2026
- Final Revised: May 29, 2026
- Accepted: Jun 17, 2026
- Published: Jun 30, 2026
- Abstract view: 9
- PDF Download: 1
- Volumes: Vol. 4 No. 1 (2026) Pages 23-42
- DOI: 10.63208/21018-240
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This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Copyright (c) 2026 Author(s)
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Abstract
Predicting cryptocurrency prices is inherently difficult due to extreme volatility influenced by market dynamics and investor sentiment. Traditional models often struggle to synthesize short-term fluctuations with long-range temporal dependencies. This study proposes L-FED, a hybrid deep learning architecture that integrates Long Short-Term Memory (LSTM) networks with the FEDformer framework, further enhanced by sentiment analysis. Utilizing a parallel learning structure, L-FED facilitates bidirectional information interaction through local-global collaborative learning. A robust feature engineering process is employed, incorporating historical trade data, technical indicators, and sentiment features alongside LSTM-derived guiding prices. Experimental results indicate that L-FED significantly outperforms baseline models. On Bitcoin and Ethereum datasets, the model achieved RMSE and MAPE improvements of up to 16% and 12.8%, respectively. Furthermore, incorporating the domain-specific CryptoBERT model for sentiment analysis improved predictive accuracy by up to 19%, demonstrating the critical value of specialized linguistic pre-training in capturing market psychology within the highly volatile cryptocurrency ecosystem.
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