Crypto-Visionary Price Forecasting System

Abstract

The utilization of machine learning techniques for predicting cryptocurrency prices has become increasingly prominent. Researchers have investigated a variety of methods, including recurrent neural networks, deep learning architectures, Bayesian regression, k-nearest neighbors, and support vector machines, to forecast prices for cryptocurrencies such as Bitcoin, Ethereum, Dogecoin, and Litecoin, etcetera. This research draws from existing studies on price prediction across different domains, including the predictability of sales, fluctuations of sale prices, gold price forecasting, and silver price predictions. The focus has been on exploiting high-dimensional features and time-series analysis while comparing various statistical and machine learning models. Models have also incorporated factors such as market liquidity and exchange dynamics. Although current literature acknowledges the potential of these methods in predicting cryptocurrency, gold and silver, there is a noted gap in applying these techniques to a wider range of cryptocurrencies. Crypto-Visionary will integrate a variety of machine learning and statistical techniques to forecast prices for cryptocurrencies, gold and silver, considering factors like market trends, trading networks, and visual attributes. Additionally, the importance of feature engineering and sample dimension manipulation is emphasized to improve the accuracy and reliability of predictions. As the cryptocurrency market evolves, there is a growing need for further research to develop robust models capable of forecasting prices for a diverse set of cryptocurrencies, thereby advancing the field.