Linear, Machine Learning and Probabilistic Approaches for Time Series Analysis
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Abstract (arXiv)
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.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 26 Feb 2017.
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Citing papers per year: 2022: 5.
5 papers.
- 2022Sales Time Series Analytics Using Deep Q-Learningcited by 3
- 2022The multi-modal universe of fast-fashion: the Visuelle 2.0 benchmarkcited by 3
- 2022Bayesian Regression Approach for Building and Stacking Predictive Models in Time Series Analyticscited by 2
- 2022Forecasting of Non-Stationary Sales Time Series Using Deep Learningcited by 1
- 2022Analytics of Business Time Series Using Machine Learning and Bayesian Inferencecited by 1
- 2022Analytics of Business Time Series Using Machine Learning and Bayesian Inference25 May 2022
- 2022Forecasting of Non-Stationary Sales Time Series Using Deep Learning23 May 2022
- 2022The multi-modal universe of fast-fashion: the Visuelle 2.0 benchmark14 Apr 2022
- 2022Sales Time Series Analytics Using Deep Q-Learning6 Jan 2022
- 2022Bayesian Regression Approach for Building and Stacking Predictive Models in Time Series Analytics6 Jan 2022