Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting

34 code implementations • 19 Dec 2019 • Bryan Lim, Sercan O. Arik, Nicolas Loeff, Tomas Pfister

Multi-horizon forecasting problems often contain a complex mix of inputs -- including static (i. e. time-invariant) covariates, known future inputs, and other exogenous time series that are only observed historically -- without any prior information on how they interact with the target.

Interpretable Machine Learning Time Series +1