FDN: Interpretable Spatiotemporal Forecasting with Future Decomposition Networks
Keywords: Spatiotemporal, Forecast, Graph, Hydrology, Traffic, Energy
TL;DR: An interpretable forecast architecture for spatiotemporal system forecasting
Abstract: Spatiotemporal systems comprise a collection of spatially distributed yet interdependent entities each generating unique dynamic signals.
Highly sophisticated methods have been proposed in recent years delivering state-of-the-art (SOTA) forecasts but few have focused on interpretability.
To address this, we propose the Future Decomposition Network (FDN), a novel forecast model capable of (a) providing interpretable predictions through classification (b) revealing latent activity patterns in the target time-series and (c) delivering forecasts competitive with SOTA methods at a fraction of their memory and runtime cost.
We conduct comprehensive analyses on FDN for multiple datasets from hydrologic, traffic, and energy systems demonstrating its improved accuracy and interpretability.
Primary Area: learning on time series and dynamical systems
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Submission Number: 8579
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