IRO Journals
Journal of Artificial Intelligence and Capsule Networks

Adaptive Hybrid Learning for Real-Time Bitcoin Price Prediction with Comparative Model Selection

Open Access
Published: 16 October, 2026
Pages: 374-389
  • Sai Vedikaa R. , Sai Vedikaa R.
    Students, Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Ramapuram, India
    Students, Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Ramapuram, India
  • Siva Srinidhi V. , Siva Srinidhi V.
    Students, Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Ramapuram, India
    Students, Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Ramapuram, India
  • Keerthika Rupa A. , Keerthika Rupa A.
    Students, Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Ramapuram, India
    Students, Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Ramapuram, India
  • Srilekha K. , Srilekha K.
    Students, Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Ramapuram, India
    Students, Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Ramapuram, India
  • Yuvashree P. , Yuvashree P.
    Students, Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Ramapuram, India
    Students, Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Ramapuram, India
  • Sree Rathna Lakshmi N. V. S. Sree Rathna Lakshmi N. V. S.
    Professor, Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Ramapuram, India
    Professor, Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Ramapuram, India
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How to Cite

R., Sai Vedikaa, Siva Srinidhi V., Keerthika Rupa A., Srilekha K., Yuvashree P., and Sree Rathna Lakshmi N. V. S. 2026. “Adaptive Hybrid Learning for Real-Time Bitcoin Price Prediction With Comparative Model Selection”. Journal of Artificial Intelligence and Capsule Networks 8 (4): 374-89. https://doi.org/10.36548/aicn.2026.4.006.
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Keywords

Bitcoin price Prediction
Machine Learning
LSTM
Gradient Boosting
Flask
Time Series Analysis
Cryptocurrency Forecasting

Abstract

This study presents a real-time Bitcoin price forecasting framework that integrates Long Short-Term Memory (LSTM), Improved Sequential Temporal Modeling (ISTM), gradient boosting, and Flask-based deployment. The experimental dataset comprises 744 hourly BTC/USDT Spot Kline observations obtained from the Binance Public Data Repository, including Open, High, Low, Close, and Volume (OHLCV) variables. A set of technical indicators and temporal features, including moving averages, exponential moving averages, Relative Strength Index (RSI), volatility measures, and lagged variables, was derived from the historical price series. Following Min-Max normalization, the dataset was chronologically divided into 595 training observations and 149 testing observations using an 80:20 split without shuffling to preserve the temporal structure of the data. Six forecasting approaches such as Linear Regression, Random Forest, LSTM, ISTM, LSTM with Boosting, and ISTM with Boosting. Among the evaluated models, the ISTM + Boosting framework demonstrated strong predictive performance, achieving an MAE of 72.5, MSE of 19,860, RMSE of 140.9, and R² of 0.97.

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