# Linear, Machine Learning and Probabilistic Approaches for Time Series Analysis

arXiv: https://arxiv.org/abs/1703.01977 · HTML: https://mlsanity.com/paper/1703.01977/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2017.

## Built on by (most cited first, 5 of 5)

- [Sales Time Series Analytics Using Deep Q-Learning](https://mlsanity.com/paper/2201.02058/) (2022)
- [The multi-modal universe of fast-fashion: the Visuelle 2.0 benchmark](https://mlsanity.com/paper/2204.06972/) (2022)
- [Bayesian Regression Approach for Building and Stacking Predictive Models in Time Series Analytics](https://mlsanity.com/paper/2201.02034/) (2022)
- [Forecasting of Non-Stationary Sales Time Series Using Deep Learning](https://mlsanity.com/paper/2205.11636/) (2022)
- [Analytics of Business Time Series Using Machine Learning and Bayesian Inference](https://mlsanity.com/paper/2205.12905/) (2022)

## 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.