# Analytics of Business Time Series Using Machine Learning and Bayesian Inference

arXiv: https://arxiv.org/abs/2205.12905 · HTML: https://mlsanity.com/paper/2205.12905/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 114 references that are in the map.
- First posted on arXiv 25 May 2022.

## Builds on (most cited first, 19 of 19)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks](https://mlsanity.com/paper/1704.04110/) (2017)
- [Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting](https://mlsanity.com/paper/1912.09363/) (2019)
- [N-BEATS: Neural basis expansion analysis for interpretable time series forecasting](https://mlsanity.com/paper/1905.10437/) (2019)
- [Deep Reinforcement Learning in Large Discrete Action Spaces](https://mlsanity.com/paper/1512.07679/) (2015)
- [A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem](https://mlsanity.com/paper/1706.10059/) (2017)
- [Financial Trading as a Game: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/1807.02787/) (2018)
- [Reinforcement Learning for Fair Dynamic Pricing](https://mlsanity.com/paper/1803.09967/) (2018)
- [Bayesian regression and Bitcoin](https://mlsanity.com/paper/1410.1231/) (2014)
- [Methods of Informational Trends Analytics and Fake News Detection on Twitter](https://mlsanity.com/paper/2204.04891/) (2022)
- [Linear, Machine Learning and Probabilistic Approaches for Time Series Analysis](https://mlsanity.com/paper/1703.01977/) (2017)
- [Forming Predictive Features of Tweets for Decision-Making Support](https://mlsanity.com/paper/2201.02049/) (2022)
- [Can Twitter Predict Royal Baby's Name ?](https://mlsanity.com/paper/1310.3500/) (2013)
- [Sales Time Series Analytics Using Deep Q-Learning](https://mlsanity.com/paper/2201.02058/) (2022)
- [Bayesian Regression Approach for Building and Stacking Predictive Models in Time Series Analytics](https://mlsanity.com/paper/2201.02034/) (2022)
- [Bitcoin Price Predictive Modeling Using Expert Correction](https://mlsanity.com/paper/2201.02729/) (2022)
- [Forecasting of Non-Stationary Sales Time Series Using Deep Learning](https://mlsanity.com/paper/2205.11636/) (2022)

## Built on by (most cited first, 1 of 1)

- [Financial News Analytics Using Fine-Tuned Llama 2 GPT Model](https://mlsanity.com/paper/2308.13032/) (2023)

## Abstract (arXiv)

In the survey we consider the case studies on sales time series forecasting, the deep learning approach for forecasting non-stationary time series using time trend correction, dynamic price and supply optimization using Q-learning, Bitcoin price modeling, COVID-19 spread impact on stock market, using social networks signals in analytics. The use of machine learning and Bayesian inference in predictive analytics has been analyzed.