Linear, machine learning and probabilistic approaches for time series analysis

Abstract

In this paper we study different approaches for time series modeling. The forecasting approaches using linear models, ARIMA algorithm, XGBoost machine learning algorithm are described. Results of different model combinations are shown. For probabilistic modeling the approaches using copulas and Bayesian inference are considered.