Khan, Mubariz and Siddiqui, Hafeez Ur Rehman and Saleem, Adil Ali and Raza, Muhammad Amjad and Hernández Rodríguez, Lázaro Javier and Herrero García, Pablo and de la Torre Díez, Isabel UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, lazaro.hernandez@uneatlantico.es, pablo.herrero@uneatlantico.es, UNSPECIFIED (2026) Cryptocurrency Market Trends: A Machine Learning-Driven Time Series Forecasting with Twitter Sentiment Integration. Computers, Materials & Continua, 88 (3). pp. 1-10. ISSN 1546-2226
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Abstract
Accurate forecasting of cryptocurrency prices remains an open challenge because classical statistical models cannot capture the non-linear, sentiment-driven dynamics of these markets. This study compares three hybrid deep learning architectures—VAR-LSTM, XGBoost-LSTM, and CNN-LSTM—to determine which best forecasts Bitcoin (BTC), Ethereum (ETH), and Dogecoin (DOGE) closing prices, and to quantify the marginal predictive value of Twitter sentiment integration. Six years of hourly OHLCV data (2017–2023) are augmented with VADER-scored Twitter sentiment polarity. Each model is formulated mathematically, implemented with documented hyperparameters (epochs, dropout, units;), and trained for one-step-ahead next-hour price prediction. Performance is measured by RMSE, MAE, MAPE, R 2 , and Directional Accuracy (DA) across five random seeds, with paired Wilcoxon significance tests. XGBoost-LSTM achieves the best performance (RMSE = 81.547, R 2 = 0.9254, DA = 80.0%), outperforming all nine literature baselines. Removing Twitter sentiment degrades DA by 14.3 percentage points ( p < 0.01 ), confirming that social media signals carry independent predictive information. Hybrid architectures consistently outperform single-model baselines; XGBoost-LSTM offers the best accuracy-to-compute ratio. VADER-enriched Twitter sentiment is a significant predictor beyond price history. Limitations include reliance on a single sentiment platform and a training window that predates several structural market events
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Cryptocurrency forecasting; long short-term memory; XGBoost; convolutional neural network; vector autoregression; sentiment analysis; Bitcoin; time series; hybrid models; deep learning |
| Subjects: | Subjects > Engineering |
| Divisions: | Europe University of Atlantic > Research > Articles and books Fundación Universitaria Internacional de Colombia > Research > Articles and books Ibero-american International University > Research > Articles and books Ibero-american International University > Research > Articles and Books Universidad Internacional do Cuanza > Research > Articles and books University of La Romana > Research > Scientific Production |
| Depositing User: | Sr Bibliotecario |
| Date Deposited: | 04 Sep 2026 08:08 |
| Last Modified: | 04 Sep 2026 08:08 |
| URI: | https://repositorio.funiber.org/id/eprint/29685 |
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