  In this paper we study different approaches for time series modeling. The
forecasting approaches using linear models, ARIMA alpgorithm, 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.
