Department of Computer Science (Artificial Intelligence and Machine Learning), ACE Engineering College, Hyderabad, Telangana, India.
Research Article
World Journal of Advanced Research and Reviews, 2024, 22(01), 375-380
Article DOI: 10.30574/wjarr.2024.22.1.1052
Received on 24 February 2024; revised on 02 April 2024; accepted on 05 April 2024
A type of digital currency known as a cryptocurrency allows all transactions to be completed online. There is no hard cash version of this soft currency. We highlight that a decentralized currency differs from a centralized currency in the any user of a virtual currency can purchase services without the need for third parties to get involved. Due to its extreme price volatility, using these cryptocurrencies has an impact on trade and international relations. Moreover, the constantly fluctuating oscillations indicate the urgent need for a more precise method of predicting this price. Deep learning techniques that use effective learning models for training data, including the LSTM, GRU, and Feedback Neural Network, can be used to do this. Benchmark datasets are used to test the suggested strategy. That brings us to the neural network, one of the clever data mining technologies that researchers in many domains have been using for the past ten years.
In the current economy, stock market data is essential. There are two types of forecasting methodologies: nonlinear models (ARCH, GARCH, Neural Network) and linear models (AR, MA, ARIMA, ARMA). To forecast a company's stock price based on past prices, we employed the Box Jenkins Model also known as ARIMA, and Long Short-Term Memory (LSTM), and Feedback Neural Network also known as RNN.
Cryptocurrency; Price Prediction; Recurrent Neural Network (RNN); Long Short-Term Memory (LSTM); Time Series Analysis; Historical Data.
Preview Article PDF
S. Venkatesh, B Rashmitha, S Manjunadha and Md Junaid. A survey on cryptocurrency price prediction. World Journal of Advanced Research and Reviews, 2024, 22(1), 375-380. Article DOI: https://doi.org/10.30574/wjarr.2024.22.1.1052