Improving the Prediction and Fine-Grained Approach for Predicting and Forecasting Cryptocurrency Price Using ARIMA

Abstract

A capacity of foreseeing price fluctuations in bitcoin with exceptionally precise is very worthwhile to investigators and funding sources. However, as the cryptocurrency market is nonlinear, it can be challenging to determine the distinctive features of time-series data, which renders it challenging to forecast accurate price estimations. Massive oscillations in non-stationary cryptocurrency values underscore the pressing necessity to precise forecasting models. The most effective methodology for cryptocurrency price forecasting is machine learning, foremost ensemble and deep learning. Traditional statistical methods are difficult to execute accurately because to the lack of seasonal variations and the need to meet a number of naive requirements. The suggested methodology builds upon the random walk theory, commonly utilized in financial markets to model stock values. To simulate market volatility, this methodology utilizes randomization into the observed feature activations of neural networks at a layer-wise level. Moreover, a mechanism to assess the market’s reaction pattern is incorporated into the prediction model. Training was conducted on ARIMA and LSTM, short for Long Short-Term Memory models using Ripple, Ethereum, and Tron as illustrative examples.