Using GMM-HMM (Gaussian Mixture Model) to predict the state of the cryptocurrency market based on historical data

Abstract

In recent years, the popularity of the cryptocurrency market has increased; two examples of well-known cryptocurrencies
are Bitcoin and Ethereum. Aim of this paper is to examine the (GMM-HMM) Gaussian Mixture Model-Hidden Markov
Model ’s potential for forecasting the bitcoin market. Effective forecasting is essential for making well-informed investing
decisions given the volatility and uncertainty of the market. The GMM-HMM model can be used to forecast the bitcoin
market because it can take into account stochastic elements and ambiguity as well as examine several possible outcomes.
The effectiveness of the GMM-HMM model will be assessed along with its use in predicting future bitcoin price values
and market trends. In order to learn more about prices, sales volume, market capitalization, and other potential market
influences for bitcoin, historical market data will be examined. Statistical and correlation analysis will then be performed
to look for any relevant relationships between these factors. The Prophet library’s forecasts were more accurate with
a smaller variation from the actual exchange rate than the GMM-HMM model’s, which had predictions that were not
quite as accurate. By incorporating other variables into the model, such as news sentiment analysis, or by experimenting
with different time series forecasting methods.