Linear, Machine Learning and Probabilistic Approaches for Time Series Analysis

2017/02/26 by Bohdan M. Pavlyshenko, Pavlyshenko, B. M.
Computer Science · Decision Sciences · #Applications (stat.AP) #FOS: Computer and information sciences #Forecasting Techniques and Applications #Machine Learning (cs.LG) #Methodology (stat.ME) #Stock Market Forecasting Methods #Time Series Analysis and Forecasting

paper · pdf · doi:10.48550/arxiv.1703.01977

openalex created_date 2016/10/14 · openalex publication_date 2017/02/26 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• Stochastic gradient boosting

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