Machine Learning Strategies for Accurate Cyrptocurrency Forecasting

Abstract

The cryptocurrency trend is gearing up among investors these days as they want to escape the boring norms of fiat money. The features like secured transactions, no intermediaries, and high speed have paved the way for the growth of cryptocurrencies across the globe. There may be a sudden rise or fall in the value and it is difficult to predict them. This might be a huge challenge for the investors as they can face a huge loss. In the proposed work, the price of the various cryptocurrencies like Bitcoin, Ethereum, Litecoin, Binance Coin, and Maker is forecasted by considering the different parameters that influence the price. The dataset is collected till the current date with the open, high, low, and close prices of the cryptocurrencies. For the price prediction, different machine learning algorithms like linear regression, support vector regression, SGD regression, lasso regression, XG boost regression, ridge regression, and random forest regression are used and compared their performance. These regression algorithms are chosen because of their predictive analysis and by comparing them we can find the best fit for the data. The prediction is improved by using the regression algorithms as they are great for forecasting because of their exploratory nature between the data points which will, in turn, predict both long-term and short-term values with fewer errors. This helps in predicting the price of the cryptocurrency more precisely and accurately by which the investors and beginners can be easily able to choose and invest in a way more profitable.