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The high level of unpredictability exhibited by cryptocurrency markets has posed significant challenges for speculators and investors seeking to predict price swings. This study examines the possibility of using machine and deep learning methods to forecast cryptocurrency values. Gathering historical data on bitcoin prices, trade volumes, investor sentiment, and other relevant factors is essential to our approach. Prior to analysis, the data undergoes cleansing, normalization, and conversion into a suitable format. Feature engineering approaches are employed to extract relevant qualities, such as sentiment analysis scores and technical indications, that might influence price movements. Afterwards, a variety of ML and DL models are selected and trained to predict future bitcoin values. The findings of our study demonstrate the efficacy of these techniques in generating accurate forecasts of cryptocurrency values, despite the highly impulsive and constantly evolving nature of the market. We highlight the need on continuously enhance and adjust forecasting models to accommodate evolving marketplace conditions as we investigate the impacts of our discoveries. In this study, we explore the efficacy of various machine learning models in predicting Bitcoin prices, emphasizing the importance of model selection and training optimization. Our investigation covers traditional algorithms like Random Forest and ARIMA, alongside advanced neural networks including CNN and LSTM_RNN, culminating in a hybrid LSTM_RNN_RANDOMFOREST model. Through a comparative analysis, the hybrid model demonstrates superior performance, as indicated by the highest R-squared value and the lowest error metrics, suggesting an optimal balance between capturing temporal patterns and generalizing beyond training data. This research not only confirms the potential of LSTM to effectively harness time-series data but also highlights the advantages of integrating it with Random Forest to bolster predictive accuracy. Performance trends across varying training epochs reveal a non-linear relationship, with an optimal epoch identified where predictive performance peaks before declining due to overfitting. The study's findings advocate for the nuanced application of hybrid models in financial forecasting, underscoring the critical balance between learning capabilities and the model's capacity to generalize, which is essential for navigating the volatile cryptocurrency market.

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Advanced Predictive Analytics for Cryptocurrency Forecasting by DEEP LSTM- RNN Network with Random Forest Regression

  • 会议论文

  • 发表日期：

    2026年

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  • Akhil Verma,
  • Ranjan Walia,
  • Vaneet Kumar

摘要

The high level of unpredictability exhibited by cryptocurrency markets has posed significant challenges for speculators and investors seeking to predict price swings. This study examines the possibility of using machine and deep learning methods to forecast cryptocurrency values. Gathering historical data on bitcoin prices, trade volumes, investor sentiment, and other relevant factors is essential to our approach. Prior to analysis, the data undergoes cleansing, normalization, and conversion into a suitable format. Feature engineering approaches are employed to extract relevant qualities, such as sentiment analysis scores and technical indications, that might influence price movements. Afterwards, a variety of ML and DL models are selected and trained to predict future bitcoin values. The findings of our study demonstrate the efficacy of these techniques in generating accurate forecasts of cryptocurrency values, despite the highly impulsive and constantly evolving nature of the market. We highlight the need on continuously enhance and adjust forecasting models to accommodate evolving marketplace conditions as we investigate the impacts of our discoveries. In this study, we explore the efficacy of various machine learning models in predicting Bitcoin prices, emphasizing the importance of model selection and training optimization. Our investigation covers traditional algorithms like Random Forest and ARIMA, alongside advanced neural networks including CNN and LSTM_RNN, culminating in a hybrid LSTM_RNN_RANDOMFOREST model. Through a comparative analysis, the hybrid model demonstrates superior performance, as indicated by the highest R-squared value and the lowest error metrics, suggesting an optimal balance between capturing temporal patterns and generalizing beyond training data. This research not only confirms the potential of LSTM to effectively harness time-series data but also highlights the advantages of integrating it with Random Forest to bolster predictive accuracy. Performance trends across varying training epochs reveal a non-linear relationship, with an optimal epoch identified where predictive performance peaks before declining due to overfitting. The study's findings advocate for the nuanced application of hybrid models in financial forecasting, underscoring the critical balance between learning capabilities and the model's capacity to generalize, which is essential for navigating the volatile cryptocurrency market.