Intersecting reinforcement learning and deep factor methods for optimizing locality and globality in forecasting: A review

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@Article{SOUSA:2024:engappai,

• author = "Joao Sousa and Roberto Henriques",
• title = "Intersecting reinforcement learning and deep factor methods for optimizing locality and globality in forecasting: A review",
• journal = "Engineering Applications of Artificial Intelligence",
• volume = "133",
• pages = "108082",
• year = "2024",
• ISSN = "0952-1976",
• DOI = "doi:10.1016/j.engappai.2024.108082",
• URL = "https://www.sciencedirect.com/science/article/pii/S0952197624002409",
• keywords = "genetic algorithms, genetic programming, ANN, Time series forecasting, Global models, Local models, Reinforcement learning, PRISMA",
• abstract = "Operational forecasting often requires predicting collections of related, multivariate time series data that are high-dimensional in nature. This can be tackled by fitting a single function to all series (global approach) or assuming each series as a separate prediction problem and fitting one function to each (local approach) - the global-local trade-off. Deep learning models inspired by different data generation processes aim to combine the benefits of global and local approaches. Specifically, these frequently propose feeding the statistical expressiveness of classical local models into more complex global networks. Following recent trends in neural networks, the theoretical foundations of these hybrid models can also explain the surge of Transformer-based time series forecasting applications, which showcase potential benefits for the global and local equilibrium. Dynamic reinforcement learning (RL) models have also been explored to optimize the balance of global and local signals in general prediction problems, frequently through Q-learning algorithms. RL models can be proposed to dynamically adjust the influence of global versus local information to improve predictive performance at both scales. This paper conducted a concise literature review focused on these two research streams to optimize the balance between globality and locality in forecasting collections of time series. It focuses on their evolution across time and hints at opportunities to close some of the research gaps by intersecting both propositions. We followed the Preferred Reporting Items for Systematic Reviews and Meta-analyzes (PRISMA) guidelines and achieved a selection of 143 publications since 2000. The main findings reveal that global models have achieved strong expressiveness in capturing the most complex structural patterns while still enabling probabilistic outcomes to be delivered through uncertainty estimates. On the other hand, RL based methods depict great benefits in mitigating the risks of generalization by imprinting contextual diversity when predicting each step ahead for each series. Within those, the adoption of other computational learning or evolutionary-based methods (e.g. Genetic Programming) to improve the parametrization of the learning policies is also highlighted as an area of future work yet to be uncovered. This review advances knowledge at the intersection of two distinct yet potentially complementary research areas, identifying opportunities to combine different methodological approaches in addressing the global-local trade-off in forecasting collections of time series. This is achieved by surfacing shared limitations in current research and presenting avenues for integrating distinct methodologies, namely by further developing the theoretical underpinnings of reinforcement learning techniques. With this work we seek to enhance understanding of the relevant research landscape and help inform future solutions by establishing a foundation for collaborative work",

}

Genetic Programming entries for Joao Sousa Roberto Henriques

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