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A Review on Crypto coin price prediction

Rohit Mhatre, Govind Gupta, Aakash Kadam, Ritu Jain

Abstract


Due to its advantages over the established banking system, cryptocurrencies have experienced a sharp increase in price in
recent years, which has had a positive impact on the economy. As cryptocurrencies are organized by blockchain technology, it
inherits its properties such as Decentralization, Transparency, Immutability. In recent years, investors and stakeholders
have become increasingly interested in machine learning and AI-assisted trading. The effectiveness of machine learning
techniques in predicting the stock market leads one to believe that they may also be used to forecast cryptocurrency prices.
According to our initial research most papers/projects have taken ‘sentiment’ as an attribute, but we will be working on
real-time data. In this work, we make an effort to forecast the prices of cryptocurrencies, primarily bitcoin and ethereum.
For the prediction job, well be using the Long Short-Term Memory (LSTM) model. Trading cryptocurrency prices is a
popular kind of exchange now. Both investors and day traders will greatly benefit from using the suggested approach.
Facebook Prophet will be the machine learning algorithm used to predict these prices. In a Time, series prediction,
Facebook Prophet has a significant level of accuracy and quickness.


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References


Colianni, et al. Algorithmic Trading of cryptocurrencies based on twitter sentiment analysis.2015. https://cs229.stanford.edu/proj2015/029_report.pdf

Ferdiansyah, Siti Hajar Othman, Raja Zahilah Raja Md Radzi. A LSTM-Method for Bitcoin Price Prediction: A Case Study Yahoo Finance Stock Market. International Conference on Electrical Engineering and Computer Science (ICECOS). 2019; 206–210.

Byjus. Datasets [online]. Available from https://byjus.com/maths/data-sets/

Ramya N. Crypto-Currency Price Prediction using Machine Learning. In 2022 6th IEEE International Conference on Trends in Electronics and Informatics (ICOEI). 2022 Apr 28; 1455– 1458.

Franco Valencia Alfonso Gómez-Espinosa, Benjamin Valdés-Aguirre. Price Movement Prediction of Cryptocurrencies Using Sentiment Analysis and Machine Learning. Entropy. 2019; 21(6): 589.

Reka Horvath. Using Pandas and Python to Explore Your Dataset. [Online]. Real Python. Available from https://realpython.com/pandas-python-explore-dataset/

Sepp Hochreiter, Jurgen Schmidhuber. Long Short-term Memory. Neural Comput. 1997; 9(8): 1735–1780.

Lamon, Nielsen, Redondo. Cryptocurrency price prediction using social media sentiments. 2017. https://cs229.stanford.edu/proj2017/final-posters/5138197.pdf

Gadgets360. Cryptocurrency Investment 101: How to Calculate Moving Average and Why is it Important. [Online]. Available from https://gadgets360.com/cryptocurrency/news/movingaverage-calculate-cryptocurrency-investment-trading-tools-tips-dogecoin-how-to-2528622

Jaya Vaidhyanathan, Aashika Jain. (2022). What Is Cryptocurrency and How Does It Work? [Online]. Available from http://web.archive.org/web/20220607025813/

https://www.forbes.com/advisor/in/investing/what-is-cryptocurrency-and-how-does-it-work/


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