crypto price prediction using lstm+xgboost

TL;DR

This paper introduces a hybrid LSTM and XGBoost model for cryptocurrency price prediction, leveraging temporal dependencies and nonlinear relationships, and demonstrates its superior performance over traditional methods across multiple cryptocurrencies.

Contribution

The study presents a novel hybrid deep learning and machine learning approach combining LSTM and XGBoost for improved cryptocurrency price forecasting.

Findings

Hybrid model outperforms standalone models and traditional methods.

Incorporating sentiment and macroeconomic features improves accuracy.

Model performs consistently across different cryptocurrencies.

Abstract

The volatility and complex dynamics of cryptocurrency markets present unique challenges for accurate price forecasting. This research proposes a hybrid deep learning and machine learning model that integrates Long Short-Term Memory (LSTM) networks and Extreme Gradient Boosting (XGBoost) for cryptocurrency price prediction. The LSTM component captures temporal dependencies in historical price data, while XGBoost enhances prediction by modeling nonlinear relationships with auxiliary features such as sentiment scores and macroeconomic indicators. The model is evaluated on historical datasets of Bitcoin, Ethereum, Dogecoin, and Litecoin, incorporating both global and localized exchange data. Comparative analysis using Mean Absolute Percentage Error (MAPE) and Min-Max Normalized Root Mean Square Error (MinMax RMSE) demonstrates that the LSTM+XGBoost hybrid consistently outperforms standalone models and traditional forecasting methods. This study underscores the potential of hybrid architectures in financial forecasting and provides insights into model adaptability across different cryptocurrencies and market contexts.