International Journal of Science, Engineering and TechnologyAuthors: Vivek Kumar, Vinod Rathi, Vineet Salar
Abstract: Bit coin is a type of digital currency that is used for online transactions. It is a digital currency that does not exist in hard currency form. Our focus is on the distinction between a decentralized currency and a centralized currency, which means that all virtual currency users can acquire services without the aid of a third party. Due to their severe price volatility, the use of these crypto currencies has an impact on international relations and trade. A reliable method for estimating this price is urgently necessary due to the rapid variations in the prices of crypto currencies. The level of one main or central control over them has been significantly affected by price control by a number of organizations, affecting relationships with other businesses and international trade. In addition, the constant oscillations suggest that a more precise method of estimating this price is urgently required. Thus, using deep learning techniques such as the recurrent neural network (RNN) and the long short-term memory (LSTM), gated recurrent unit (GRU), which are effective learning models for training data, we must design a method for the accurate prediction of by considering various factors such as market cap, maximum supply and, volume, circulating supply. Python is used to write the proposed method and it is tested on benchmark datasets. It can be inferred from the results that the proposed method is capable of making reliable predictions. For the past ten years, academics in various fields have used neural networks as one of the intelligent data mining tools. The importance of stock market data cannot be overstated in today's economy. Forecasting methodologies can be divided into two types: linear (AR, MA, ARIMA, ARMA) and nonlinear models (ARCH, GARCH, Neural Network). To anticipate a company's stock price based on past prices, we employed Autoregressive Integrated Moving Average (ARIMA), Recurrent Neural Network, Long Short-Term Memory (LSTM), and Gated Recurrent Unit Deep Learning Architectures (GRU).
DOI: http://doi.org/10.5281/zenodo.20338842
