The forecast performances of classical time series models and machine learning algorithms on bitcoin series using exogenous variables

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Sevilay_Dogan_thesis.pdf

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2022-11-30

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Doğan, Sevilay

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Time series analysis importantly gives insight into what happens to a time series on any subject for days, weeks, months or years. Bitcoin is the most popular technology since it has exclusive attention in economics and finance. In this study, some of the approaches are investigated about forecasting and modeling the most popular cryptocurency bitcoin prices.The performance of classical time series methods and machine learning algorithms are compared in the study. As classical time series models, Autoregressive Integrated Moving Average (ARIMA) and Holt’s Exponential Smoothing methods are used, and Prophet, Bayesian Neural Network, Feed Forward Neural Network and Long Short Term Memory and Random Forest are the methods used as machine learning algortihms. The study period is chosen from 2019-07-30 to 2021-10-19 as daily. The bitcoin prices are predicted with exogenous variables which are ethereum and tether two top cryptocurrencies, economic and technological variables via these models. According to forecast performances of the models, machine learning methods mostly outperform the classical time series methods. With Random Forest algorithm, a very good forecast perfromance is obtained with the exogenous variables on the bitcoin prices.

Subject Keywords

Time series analysis, Forecast, Machine learning, Cryptocurrency

URI

https://hdl.handle.net/11511/101307

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Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

S. Doğan, “The forecast performances of classical time series models and machine learning algorithms on bitcoin series using exogenous variables,” M.S. - Master of Science, Middle East Technical University, 2022.