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Linear, Machine Learning and Probabilistic Approaches for Time Series Analysis
February 26, 2017 Β· Declared Dead Β· π International Conference on Data Stream Mining & Processing
"No code URL or promise found in abstract"
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Authors
B. M. Pavlyshenko
arXiv ID
1703.01977
Category
stat.AP
Cross-listed
cs.LG,
stat.ME
Citations
41
Venue
International Conference on Data Stream Mining & Processing
Last Checked
6 months ago
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.
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