Using GMM-HMM (Gaussian Mixture Model) to predict the state of the cryptocurrency market based on historical data
Year: 2025
Type: article
Abstract: In recent years, the popularity of the cryptocurrency market has increased; two examples of well-known cryptocurrenciesare Bitcoin and Ethereum. Aim of this paper is to examine the (GMM-HMM) Gaussian Mixture Model-Hidden MarkovModel ’s potential for forecasting the bitcoin market. Effective forecasting is essential for making well-informed investingdecisions given the volatility and uncertainty of the market. The GMM-HMM model can be used to forecast the bitcoinmarket 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 valuesand market trends. In order to learn more about prices, sales volume, market capitalization, and other potential marketinfluences for bitcoin, historical market data will be examined. Statistical and correlation analysis will then be performedto look for any relevant relationships between these factors. The Prophet library’s forecasts were more accurate witha smaller variation from the actual exchange rate than the GMM-HMM model’s, which had predictions that were notquite as accurate. By incorporating other variables into the model, such as news sentiment analysis, or by experimentingwith different time series forecasting methods.
Authors: Norbert Błaszczyk, Adrian Michalik
Institution: Opole University of Technology
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4414751020
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