Graph-Enabled Reinforcement Learning for Time Series Forecasting With Adaptive Intelligence

Abstract

Reinforcement learning (RL) is renowned for its proficiency in modeling sequential tasks and adaptively learning latent data patterns. Deep learning models have been extensively explored and adopted in regression and classification tasks. However, deep learning has limitations, such as the assumption of equally spaced and ordered data, and the inability to incorporate graph structure in time-series prediction. Graph Neural Network (GNN) can overcome these challenges by capturing the temporal dependencies in time-series data effectively. In this study, we propose a novel approach for predicting time-series data using GNN, augmented with Reinforcement Learning(GraphRL) for monitoring. GNNs explicitly integrate the graph structure of the data into the model, enabling them to naturally capture temporal dependencies. This approach facilitates more accurate predictions in complex temporal structures, as encountered in healthcare, traffic, and weather forecasting domains. We further enhance our GraphRL model's performance through fine-tuning with a Bayesian optimization technique. The proposed framework surpasses baseline models in time-series forecasting and monitoring. This study's contributions include introducing a novel GraphRL framework for time-series prediction and demonstrating GNNs' efficacy compared to traditional deep learning models, such as Recurrent Neural Networks (RNN) and Long Short-Term Memory Networks(LSTM). Overall, this study underscores the potential of GraphRL in yielding accurate and efficient predictions within dynamic RL environments.