Sales Time Series Analytics Using Deep Q-Learning (2201.02058v1)

Abstract: 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.

• The paper introduces a model-free deep Q-learning framework that optimizes pricing strategies by simulating sales through a logistic function.
• It demonstrates how epsilon-greedy policies and experience replay balance exploration and exploitation in adapting to dynamic supply-demand scenarios.
• The study highlights future potential for integrating complex environments to further refine retail analytics and dynamic pricing mechanisms.

Sales Time Series Analytics Using Deep Q-Learning

The paper "Sales Time Series Analytics Using Deep Q-Learning" presents an application of deep Q-learning models to the domain of sales time series analytics. This research highlights the distinction between traditional supervised learning approaches, which are typically used for predictive analytics based on historical observations, and reinforcement learning, which is designed to maximize cumulative rewards through sequences of actions. Specifically, the paper explores the model-free deep Q-learning paradigm to address challenges in optimizing pricing strategies and supply-demand management within the sales domain.

Methodology and Experiments

The deep Q-learning approach utilized in this paper provides a framework for agents to interact with a modeled environment and optimize decision-making processes by maximizing a reward function. This aspect is critical, as it allows the creation of adaptive pricing strategies and efficient management of supply-demand dynamics.

1. Optimal Pricing Strategy:
   • The research introduces a simplistic model to simulate the environment where sales depend on the extra price. The model utilizes a logistic function to investigate the relationship between sales volume and price increments.
   • Q-learning is implemented using a neural network to approximate Q-values, with reward functions tied to sales profits as a function of pricing strategy.
   • Numerical experiments demonstrate the transition from a broad exploratory phase to a more focused exploitation phase, optimizing the pricing strategy that maximizes profit.
2. Supply and Demand Optimization:
   • The paper explores Q-learning's efficacy in managing supply-demand scenarios using historical demand data simulated from the 'Rossmann Store Sales' dataset.
   • The experiments factor in additional variables such as promo actions and week-based seasonality to enhance the representation of states within the Q-learning framework.
   • The results show multiple actions become optimal, distinctly influenced by the state variables and demand patterns.

The experiments applied epsilon-greedy policies to balance exploration and exploitation. The use of experience replay mechanisms aids in stabilizing and averaging the training experiences over episodic agent-environment interactions.

Implications and Future Directions

The research contributes to the practical implementation of reinforcement learning methods, demonstrating that deep Q-learning can facilitate effective decision-making in business analytics, specifically in dynamic pricing and inventory management. The ability to simulate environments using historical data or parametric models supports agent training and a gradual introduction to real-world problems.

In the future, this approach could be expanded to integrate more complex modeling of environments, such as incorporating perishable goods dynamics or multi-product pricing strategies. Moreover, the adaptability of the Q-learning agent to changing environments presents opportunities for continued evolution in retail and sales management strategies.

Overall, this paper establishes a valid methodological basis for employing deep Q-learning in sales analytics, showing promising results that encourage further exploration and refinement within this sector.