Method evidence record
Temporal Fusion Transformer
The Temporal Fusion Transformer (TFT), introduced by Lim, Arık, Loeff and Pfister in 2021, is an interpretable deep learning architecture for multi-horizon time series forecasting. It combines variable selection, gating, multi-horizon attention and quantile outputs, processing static, past and known-future inputs together to produce multi-step forecasts.
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Temporal Fusion Transformer for Interpretable Multi-Horizon Time Series Forecasting
Taxonomic method record · ml-model / deep-learning
- Lim, B., Arık, S. Ö., Loeff, N. & Pfister, T. (2021). Temporal Fusion Transformers for Interpretable Multi-Horizon Time Series Forecasting. International Journal of Forecasting, 37(4), 1748–1764. · DOI 10.1016/j.ijforecast.2021.03.012
- Lim, B. & Zohren, S. (2021). Time-Series Forecasting with Deep Learning: A Survey. Philosophical Transactions of the Royal Society A, 379(2194), 20200209. · DOI 10.1098/rsta.2020.0209
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