Leveraging Spatio-Temporal Locality in Linear Model Trees for Multi-Step Time Series Forecasting

In the era of Big Data, forecasting the future measurements of geo-distributed sensors can be considered among the most fundamental tasks for several application domains. However, the spatial distribution of such sensors introduces several challenges for forecasting methods. The most important one is due to the spatial dimension which introduces autocorrelation phenomena, according to which nearby locations are not independent and should not be treated as such by the learning algorithms. Some existing approaches are able to capture the spatial autocorrelation, but they tend to model spatial information globally across all locations. On the contrary, our method, called SPLiT, focuses on capturing spatial information only among time series with similar trends, also at different timing, modeling the so-called spatio-temporal locality. The proposed method is based on linear model trees, which allow us to naturally model any form of discontinuities in the spatial autocorrelation during the tree growing phase, guided by heuristics that identify time series with similar trends. SPLiT works in the multi-step predictive setting in order to simultaneously provide forecasts for multiple time steps for multiple sensors in the future.Our experiments, conducted on two real-world datasets, show the effectiveness of the proposed method in forecasting the energy production of geographically distributed renewable power plants. The comparison against several tree-based models and state-of-the-art neural networks that consider both temporal and spatial dimensions shows the superiority of the proposed method.