• Bohdan M. Pavlyshenko


Sales, Time Series, deep Q-learning, Reinforcement Learning, Machine Learning


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, where historical data are used, Q-learning is a kind of active learning aimed at maximizing a reward by optimal sequence of actions. Model free Q-learning approach to optimal pricing strategies and supply-demand problems is considered in the work. The main idea of the study is to show that using deep Q-learning approach in time series analytics causes the sequence of actions to 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.


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How to Cite

Pavlyshenko, B. M. (2020). SALES TIME SERIES ANALYTICS USING DEEP Q-LEARNING. International Journal of Computing, 19(3), 434-441. Retrieved from