  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.
