BITCOIN PRICE PREDICTION WITH COVID-19 SENTIMENT USING LSTM NEURAL NETWORK

Shachi Bhavsar; Ravi Gor

doi:10.29121/ijoest.v6.i4.2022.355

Authors

Bhavsar Shachi Research Scholar, Department of Mathematics, Gujarat University, Ahmedabad-380009
Ravi Gor Department of Mathematics, Gujarat University, Ahmedabad-380009

DOI:

https://doi.org/10.29121/ijoest.v6.i4.2022.355

Keywords:

Supervised learning, Neural Networks, Back Propagation, Long Short term Memory, Gated Recurrent Unit

Abstract

Cryptocurrencies are nowadays getting popular for investment due to its various benefits such as low transaction cost, blockchain secured platform, profit, etc. Bitcoin being top of the market capitalization currency, gained more popularity during covid-19 pandemic. This study focuses on bitcoin price prediction with covid-19 sentiment. Here Long Short Term Memory Deep learning model based on machine learning is used for price prediction. At the end both results i.e., with covid-19 sentiment and without it are compared which shows model performs better by adding sentiments.

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References

Achyut, G. Soumik, B. Giridhar, M. Narayan, C. & Somya, S. (2019). Stock Price Prediction Using LSTM on Indian Share Market. 32nd International Conference on Computer Applications in Industry and Engineering, 63, 101-110. https://easychair.org/publications/open/LKgn

Alvin, H. Ramesh, V. & Kumar, R. S. (2021). Bitcoin Price Prediction Using Machine Learning and Artificial Neural Network Model. Indian Journal Of Science And Technology, 2300-2308. https://doi.org/10.17485/IJST/v14i27.878

Aniruddha, D. Kumar, S. & Meheli, B. (2020). A gated recurrent unit approach to bitcoin price prediction. Journal of Risk and Financial Management, MDPI. https://arxiv.org/abs/1912.11166#:~:text=In%20today's%20era%20of%20big,predict%20Bitcoin%20price%20and%20volatility.

Bhavsar, S. & Gor, R. (2022). Comparison of Back propagation algorithms: Bidirectional GRU and Genetic Deep Neural Network for Churn Customer. International Organization of Scientific Research Journal of Computer Engineering (IOSR-JCE).

Bhavsar, S. & Gor, R. (2022). Predicting Restaurant Ratings using Back Propagation Algorithm. International Organization of Scientific Research journal of Applied Mathematics (IOSR-JM), 18(2), 5-9. https://iosrjournals.org/iosr-jm/papers/Vol18-issue2/Ser-2/C1802021014.pdf

Ethereum Races Clock to Collect Enough Coins for Big Upgrade. (2020). Retrieved from https://www.bloomberg.com/asia

Ghosh, M. & Gor, R. (2022). Ad-Campaign Analysis and Sales prediction using K-means Clustering and Random Forest Regressor. International Organization of Scientific Research Journal of Applied Mathematics, 18(2), 10-14. https://iosrjournals.org/iosr-jm/papers/Vol18-issue2/Ser-2/B1802020509.pdf

Ghosh, M. & Gor, R. (2022). Health Insurance Premium Prediction Using BlockChain Technology and Random Forest Resression Algorithm (In press). International Journal of Engineering Science Technologies (IJOEST). https://doi.org/10.29121/ijoest.v6.i3.2022.346

Ghosh, M. & Gor, R. (2022). Short Message service Classifier Application using Naive Bayes algorithm. IOSR Journal of Computer Engineering (IOSR-JCE), 24(3), 1-6.

Hochreiter, S. & Schmidhuber, J. (1997). Long Short Term Memory. Neural Computation, 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735

Ioannis, L. Kiriakidou, N. Stavroyiais, S. & Pitelas, P. (2021). An advanced CNN LSTM model for cryptocurrency forecasting. Electronics MDPI, 287 (1-16). https://www.mdpi.com/2079-9292/10/3/287

Pengfei, Y. & Yan, X. (2019). Stock price predictions based on deep neural networks. Neural computing and applications, 1609-1628. https://doi.org/10.1007/s00521-019-04212-x

Ping, H. J. Liu, J. & Jie, Y. (2021). Examaning the psychological state analysis relationship between bitcoin prices and COVID-19. Frontier in Psychology, 1-7. https://www.frontiersin.org/articles/10.3389/fpsyg.2021.647691/full

Rajpurohit, V. Bhavsar , S. & Gor, R. (2021). A comparision of GRU-based ETH price prediction. Proceeding of International Conference on Mathemaitcal Modelling and Simulation in Physical Sciences (MMSPS-2021), 424-431.

Salman, K. A. & Peter, A. (2019). Predictive Analytics in Cryptocurrency Using Neural networks: A Comparative Study. IJRTE, 7(6). https://www.researchgate.net/publication/336640118_Predictive_Analytics_in_Cryptocurrency_Using_Neural_networks_A_Comparative_Study

Shen, J. (2021). Short term stock market price trend prediction using comprehensive deep learning system. Journal of big data, 1-33. https://doi.org/10.1186/s40537-020-00333-6

Sheng, C. & Hongxiang, H. (2018). Stock Prediction Using Convolutional Neural Network. AIAAT Materials Science and Engineering, 435. https://doi.org/10.1088/1757-899X/435/1/012026

Yang, L. Zibin, Z. & Dai, H. N. (2020). Enhancing bitcoin price fluctuation predictionn using attentive LSTM and Embedding network. Applied sciences MDPI, 4872. https://doi.org/10.3390/app10144872

The Humanitarian Data Exchange (n.d.). Retrieved from data.humdata.org

Asian, C. W. (n.d.). shares end quarter in sombre mood, dollar on high Asian shares end quarter in sombre mood, dollar on high. https://www.investing.com/

impact-of-covid-19-on-cryptocurrencies. (2021). Retrieved from https://www.freepressjournal.in/business/impact-of-covid-19-on-cryptocurrencies