# Sales Time Series Analytics Using Deep Q-Learning

arXiv: https://arxiv.org/abs/2201.02058 · HTML: https://mlsanity.com/paper/2201.02058/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 28 references that are in the map.
- First posted on arXiv 6 Jan 2022.

## Builds on (most cited first, 6 of 6)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [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)
- [Linear, Machine Learning and Probabilistic Approaches for Time Series Analysis](https://mlsanity.com/paper/1703.01977/) (2017)

## Built on by (most cited first, 3 of 3)

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

The article describes the use of deep Q-learning models in the problems of sales time series analytics. In contrast to supervised machine learning which is a kind of passive learning using historical data, Q-learning is a kind of active learning with goal to maximize a reward by optimal sequence of actions. Model free Q-learning approach for optimal pricing strategies and supply-demand problems was considered in the work. The main idea of the study is to show that using deep Q-learning approach in time series analytics, the sequence of actions can be optimized by maximizing the reward function when the environment for learning agent interaction can be modeled using the parametric model and in the case of using the model which is based on the historical data. In the pricing optimizing case study environment was modeled using sales dependence on extras price and randomly simulated demand. In the pricing optimizing case study, the environment was modeled using sales dependence on extra price and randomly simulated demand. In the supply-demand case study, it was proposed to use historical demand time series for environment modeling, agent states were represented by promo actions, previous demand values and weekly seasonality features. Obtained results show that using deep Q-learning, we can optimize the decision making process for price optimization and supply-demand problems. Environment modeling using parametric models and historical data can be used for the cold start of learning agent. On the next steps, after the cold start, the trained agent can be used in real business environment.