Reinforcement learning is well known for its ability to model sequential tasks and learn latent data patterns adaptively. Deep learning models have been widely explored and adopted in regression and classification tasks. However, deep learning has its limitations such as the assumption of equally spaced and ordered data, and the lack of ability to incorporate graph structure in terms of time-series prediction. Graphical neural network (GNN) has the ability to overcome these challenges and capture the temporal dependencies in time-series data. In this study, we propose a novel approach for predicting time-series data using GNN and monitoring with Reinforcement Learning (RL). GNNs are able to explicitly incorporate the graph structure of the data into the model, allowing them to capture temporal dependencies in a more natural way. This approach allows for more accurate predictions in complex temporal structures, such as those found in healthcare, traffic and weather forecasting. We also fine-tune our GraphRL model using a Bayesian optimisation technique to further improve performance. The proposed framework outperforms the baseline models in time-series forecasting and monitoring. The contributions of this study include the introduction of a novel GraphRL framework for time-series prediction and the demonstration of the effectiveness of GNNs in comparison to traditional deep learning models such as RNNs and LSTMs. Overall, this study demonstrates the potential of GraphRL in providing accurate and efficient predictions in dynamic RL environments.

翻译：强化学习以其建模序列任务和自适应学习隐式数据模式的能力而闻名。深度学习模型已在回归和分类任务中得到广泛探索与应用。然而，深度学习存在局限性，例如假设数据等距有序，以及在时间序列预测中缺乏融入图结构的能力。图神经网络（GNN）能够克服这些挑战，捕获时间序列数据中的时间依赖性。在本研究中，我们提出了一种新颖方法，利用GNN进行时间序列预测，并结合强化学习（RL）进行监测。GNN能够将数据的图结构显式纳入模型，从而以更自然的方式捕获时间依赖性。该方法可在复杂时间结构（如医疗、交通和天气预报领域中的结构）中实现更准确的预测。我们还采用贝叶斯优化技术对GraphRL模型进行微调，以进一步提升性能。所提出的框架在时间序列预测与监测方面优于基线模型。本研究的贡献包括：引入了一种新颖的GraphRL框架用于时间序列预测，并论证了GNN相较于传统深度学习模型（如RNN和LSTM）的有效性。总体而言，本研究展示了GraphRL在动态RL环境中提供准确高效预测的潜力。