Cryptocurrency Price Prediction by Using Hybrid Machine Learning and Beetle Antennae Search Approach
Aleksandar Petrović, Ivana Strumberger, Timea Bezdan, Hothefa Shaker Jassim, Said Suleiman Nassor
Abstract
Cryptocurrencies are defined as digital mediums of exchange, that use strong cryptography for securing the transactions and verifying the ownership of the coins. Blockchain operates in the background to guarantee the security, transparency and traceability of the transactions. Consequently, cryptocurrencies became more and more popular and established their considerable presence in financial sector. However, one of the major drawbacks in the cryptocurrency market is the unreliability and unpredictability of their values, that poses a major risk for any kind of investment. Predicting the price of cryptocurrencies is therefore a hot research domain today. This paper proposes a novel method to predict the prices, that is based on a hybrid machine learning and swarm intelligence approach. The results of the conducted experiments suggest that the proposed model obtains higher accuracy than other recent similar approaches, and that it can be successfully applied for this important task.