Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting
Multi-horizon prediction problems often contain a complex mix of inputs -- including static covariates, known future inputs, and other exogenous time series -- without any prior information on how they interact with the target. While several deep learning models have been proposed for multi-step prediction, they typically comprise black-box models which do not account for the full range of inputs present in common scenarios. In this paper, we introduce the Temporal Fusion Transformer (TFT) -- a novel attention-based architecture which combines high-performance multi-horizon forecasting with interpretable insights into temporal dynamics. To learn temporal relationships at different scales, the TFT utilizes recurrent layers for local processing and interpretable self-attention layer for learning long-term dependencies. The TFT also utilizes specialized components for judicious selection of the relevant features, and series of gating layers to suppress unnecessary components -- enabling high performance in a wide range of regimes. On a variety of real-world datasets, we demonstrate performance improvements over existing benchmarks, and showcase three practical interpretability use-cases of our model.