The popularity and adoption of cryptocurrencies are increasing on a daily basis. The entire crypto-economy is dependent on computers and the internet, starting from mining to actual trading. As a young industry, crypto is still volatile and the market changes on an hourly basis, which makes predictions and market analysis harder. But being an asset, cryptocurrencies are behaving similar to stocks, which means that we can use linear regression models and combine them with intelligence from sources like social media to calculate the volatility and prices of cryptocurrencies. Because of the amount of data and dynamics of the market, high performance computing is a logical solution for training and deploying price prediction models.
Although machine learning has been successful in predicting stock market prices through a host of different time series models, its application in predicting cryptocurrency prices has been quite restrictive. The reason behind this is obvious as prices of cryptocurrencies depend on a lot of factors like technological progress, internal competition, pressure on the markets to deliver, economic problems, security issues, political factor etc. Their high volatility leads to the great potential of high profit if intelligent inventing strategies are taken.
