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International Journal for Research in Applied Science & Engineering Technology (IJRASET)
ISSN: 2321-9653; IC Value: 45.98; SJ Impact Factor: 7.538
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Volume 11 Issue III Mar 2023- Available at www.ijraset.com
B. Merits
The LSTM model helps in:
1) Predicting future values
2) Increased number of timesteps
3) Greater accuracy for forecasting
4) Better Decision making for business
V. CONCLUSION
LSTM based Recurrent neural networks are the most powerful approach for learning from sequential data, whereas the time series are only a special case. The potential of LSTM based models is fully revealed when learning from massive datasets where we can detect complex patterns. The LSTM model, implemented here is a model that takes into consideration the features that affect the Bitcoin price. This model is accurate when predicting the future prices. However, to increase the efficiency of the model, more Bitcoin price features need to be taken into consideration. I recommend using Yahoo Finance for the source of datasets, since information present in this website holds a high degree of authenticity. In my future work, I would include in-depth scrutinisation on the topic of LSTM, and deep learning at large. Such fact-findings would be beneficial for forecasting the prices of cryptocurrencies with the help of LSTM’s in the future.
References
[1] Krishna Pal Sharma, Shivam Kumar Singh, Ankur Choudhary, Himanshu Girl, "Price Prediction of Bitcoin using Social media activities and past trends" 2023, 13th International conference on Cloud computing, Data science & Engineering.
[2] Muhammad Husaini, Amgad Muneer, Shakirah Mohd Taib, "Crypto currency price prediction using LSTM with the Twitter sentiment analysis" 2022, 6th International conference on computing, communication, control and automation.
[3] Soudeh Javadi, Paras M Kathuria, Nisha S Gowda, Talha Ali Khan, "Bitcoin price prediction using LSTM", 2022, By the 3rd International conference on Automatics and Informatics.
[4] Tamara zuvela, Sara Lazarevic, Sofia Djordjevic, Marko Arsenovic, "Crypto currency price prediction using Deep Learning" 2022, IEEE 16th International Symposium on Applied computational intelligence and informatics.
[5] Chandra Sekhar, M Padmaja, Biswajit Sarangi, Aditya , "Prediction of Crypto currency using LSTM and XG BOOST" 2022 IEEE International conference on Block chain and distributed systems security.
[6] T. Phaladisailoed, and T. Numnoda, “Machine Learning Models Comparison for Bitcoin Price Prediction” Under International Conference on Information Technology and Electrical Engineering, 2018.
[7] Neha Mangla, Akshay Bhat, Ganesh Avarbratha, and Narayana Bhat, “Bitcoin Price Prediction Using Machine Learning” in the International Journal of Information and Computer Science, Volume 6, Issue 5, May 2019.
[8] A. Rana, R. Kachchhi, J. Baradia, V. Shelke “Stock Market Prediction Using Deep Learning” International Research Journal of Engineering and Technology, Volume 8, Issue 4, April 2021.
[9] Q. Guo, S. Lei, Q. Ye, Z. Fang “MRC-LSTM: A Hybrid Approach of Multi-scale Residual CNN and LSTM to Predict Bitcoin Price,” MDPI, May 2021.
[10] T. Awoke, M. Rout, L. Mohanty, S. C. Satapathy, “Bitcoin Price Prediction and Analysis Using Deep Learning Models” 2021, Research Gate.
