Post #803

Graph Deep Learning for Time Series Forecasting by Andrea Cini, Ivan Marisca, Daniele Zambon, Cesare Alippi arxiv: https://arxiv.org/abs/2310.15978 We are happy to announce the release of our paper on graph deep learning for time series forecasting. This work distills what we learned in the last few years using GNNs for time series analysis. Key Contributions: - We introduce a methodological framework that addresses foundational aspects of graph-based neural forecasting often overlooked in existing literature. - Our approach formalizes the forecasting problem in a graph-based context and offers design principles for building efficient graph-based predictors. - We discuss how spatiotemporal GNNs can take advantage of pairwise relationships by sharing parameters and conditioning forecasts on graphs spanning the time series collection. Highlights: - The paper provides an extensive overview of the field, alongside best practices and recommendations to design and evaluate predictors. - It delves into ongoing challenges such as latent graph learning, handling missing data, dealing with local effects, inductive learning, and scalability issues. Additional Resources: For those interested in practical applications, we have also developed a PyTorch library, TorchSpatiotemporal (https://github.com/TorchSpatiotemporal/tsl), aimed at simplifying the implementation of graph-based time series models. More on our work at https://gmlg.ch. We hope you find this useful for your research!