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Analysis and price prediction of cryptocurrencies for historical and live data using ensemble-based neural networks

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Abstract

The popularity of cryptocurrencies has been on the rise with the emergence of blockchain technologies. There have been enormous investments in the cryptocurrency market over the past few years. However, the volatile nature and significant price fluctuations in cryptocurrency have resulted in a high investment risk of these assets. In this paper, an improved neural network (NN) ensemble-based approach is proposed with the help of Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (LSTM), i.e., CNN-BiLSTM for long-term price prediction of cryptocurrencies using both live data API and historical data. The CNN learns internal representation of each cryptocurrency independently and extracts useful features. On the other hand, the LSTM layers are used to predict time-series data and recognize the long as well as short-term dependencies efficiently and accurately. The proposed ensemble of CNN-BiLSTM consists of three layers of CNN and two layers of Bi-LSTM to improve the accuracy of the predictions. Moreover, MLP, GRU, CNN and LSTM models are also implemented and compared with the proposed model on the test datasets followed by error evaluation. For evaluating the error of each model, Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) scores are analyzed for four cryptocurrencies Bitcoin, Ethereum, Dogecoin and Litecoin of historical and live data API. It is observed that the proposed CNN-BiLSTM ensemble model has the lowest RMSE score of 0.164 for live data API for Bitcoin and 0.166 for historical dataset for Dogecoin. The MSE score of 0.027 is observed for both Bitcoin and Dogecoin cryptocurrencies for live data API and 0.027 for Dogecoin for historical dataset. Thus, RMSE and MSE scores of the proposed approach are very less as compared to MLP, GRU, CNN and LSTM models for cryptocurrency price prediction for both the datasets.

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Authors and Affiliations

  1. Indira Gandhi Delhi Technical University for Women, Delhi, India

    Nisha Rathee,  Ankita Singh, Tanisha Sharda, Nimisha Goel,  Mansi Aggarwal & Sanya Dudeja

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  1. Nisha Rathee
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  2. Ankita Singh
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  3. Tanisha Sharda
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  4. Nimisha Goel
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Contributions

Authors TS, NG, M and SD wrote the manuscript, and authors NR and AS guided and mentored. All authors reviewed the manuscript.

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Correspondence to Ankita Singh.

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Appendix

Appendix

See Figs.

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Bitcoin price prediction graphs of (i) CNN, (ii) LSTM, (iii) GRU and (iv) MLP models, respectively, using live data

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Ethereum price prediction graphs of (i) CNN, (ii) LSTM, (iii) GRU and (iv) MLP models, respectively, using live data

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Dogecoin price prediction graphs of (i) CNN, (ii) LSTM, (iii) GRU and (iv) MLP models, respectively, using live data

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Litecoin price prediction graphs of (i) CNN, (ii) LSTM, (iii) GRU and (iv) MLP models, respectively, using live data

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Bitcoin price prediction graphs of (i) CNN, (ii) LSTM, (iii) GRU and (iv) MLP models, respectively, using historical data

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Ethereum price prediction graphs of (i) CNN, (ii) LSTM, (iii) GRU and (iv) MLP models, respectively, using historical data

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Dogecoin price prediction graphs of (i) CNN, (ii) LSTM, (iii) GRU and (iv) MLP models, respectively, using historical data

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Litecoin price prediction graphs of (i) CNN, (ii) LSTM, (iii) GRU and (iv) MLP models, respectively, using historical data

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Rathee, N., Ankita Singh, Sharda, T. et al. Analysis and price prediction of cryptocurrencies for historical and live data using ensemble-based neural networks. Knowl Inf Syst 65, 4055–4084 (2023). https://doi.org/10.1007/s10115-023-01871-0

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