Spatiotemporal graph neural network for multivariate multi-step ahead time-series forecasting of sea temperature

Abstract

This paper proposes a spatiotemporal graph neural network capable of effective representation learning of the spatiotemporal interrelationships and interdependencies of in-situ observation data from multiple locations for multivariate multi-step ahead time-series forecasting. The propose model is largely composed of graph learning, spatial encoder, and temporal decoder, and ablation studies on variants of the three modules and comparative experiments with state-of-the-art deep neural networks for sequence modeling were also performed extensively. The proposed model showed improved predictability than conventional numerical model-based approaches or state-of-the-art models by applying consecutive multi-step ahead time-series prediction of sea surface temperature at multiple locations along the coast. For more rigorous performance evaluation, not only the overall performance of the test data, but also the performance of extreme cases included in the test data based on historical records were separately assessed. The prediction rationales were also presented through quantified relative contributions between neighbor locations using the trained adjacency matrix obtained through graph learning. The results showed that it is well consistent with the ocean physics and geographical domain knowledge, demonstrating the feasibility and reliability of the proposed method. Therefore, the proposed method shows sufficient potential to be used as a scientific tool for decision-making in extreme events such as marine heat waves or for operational ocean forecasting.