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This study investigates the combination of Long Short-Term Memory (LSTM) and Autoregressive Integrated Moving Average (ARIMA) models for cryptocurrency price prediction. ARIMA excels in time-series data modeling, whereas LSTM excels in capturing long-term dependencies. We hope to improve the precision of cryptocurrency price predictions by combining these techniques. The hybrid approach uses the advantages of both models to produce reliable forecasts in the face of unstable cryptocurrency markets. We confirm the effectiveness of our method for predicting cryptocurrency values through thorough analysis and comparison, providing insightful information for stakeholders and investors in this rapidly changing financial environment.

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Cryptocurrency Price Prediction Using LSTM and ARIMA

  • 会议论文

  • 发表日期：

    2026年

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  • Anwesh Shaw,
  • Arpit Pandey,
  • Harshit Tripathi,
  • N. Senthamarai

摘要

This study investigates the combination of Long Short-Term Memory (LSTM) and Autoregressive Integrated Moving Average (ARIMA) models for cryptocurrency price prediction. ARIMA excels in time-series data modeling, whereas LSTM excels in capturing long-term dependencies. We hope to improve the precision of cryptocurrency price predictions by combining these techniques. The hybrid approach uses the advantages of both models to produce reliable forecasts in the face of unstable cryptocurrency markets. We confirm the effectiveness of our method for predicting cryptocurrency values through thorough analysis and comparison, providing insightful information for stakeholders and investors in this rapidly changing financial environment.