Temporal Fusion Transformers for interpretable multi-horizon time series forecasting

Lim, Bryan; Arık, Sercan Ö.; Loeff, Nicolas; Pfister, Tomas

doi:10.1016/j.ijforecast.2021.03.012

Cited by 3,291Time Series Analysis and ForecastingForecasting Techniques and ApplicationsTraffic Prediction and Management Techniques

Temporal Fusion Transformers for interpretable multi-horizon time series forecasting

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Lim, Bryan, Arık, Sercan Ö., Loeff, Nicolas, Pfister, Tomas·2021·International Journal of Forecasting

DOI 10.1016/j.ijforecast.2021.03.012

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Abstract

Multi-horizon forecasting often contains a complex mix of inputs – including static (i.e. time-invariant) covariates, known future inputs, and other exogenous time series that are only observed in the past – without any prior information on how they interact with the target. Several deep learning methods have been proposed, but they are typically ‘black-box’ models that do not shed light on how they use the full range of inputs present in practical scenarios. In this paper, we introduce the Temporal Fusion Transformer (TFT) – a novel attention-based architecture that combines high-performance multi-horizon forecasting with interpretable insights into temporal dynamics. To learn temporal relationships at different scales, TFT uses recurrent layers for local processing and interpretable self-attention layers for long-term dependencies. TFT utilizes specialized components to select relevant features and a series of gating layers to suppress unnecessary components, enabling high performance in a wide range of scenarios. On a variety of real-world datasets, we demonstrate significant performance improvements over existing benchmarks, and highlight three practical interpretability use cases of TFT.

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Recent work in a time-series analysis by Google has demonstrated the promise of attention-based model architectures to combine both static and time-dependent inputs to achieve interpretable time-series forecasting.
“Of note, recent work in a time-series analysis by Google has demonstrated the promise of attention-based model architectures to combine both static and time-dependent inputs to achieve interpretable time-series forecasting.”
From Multimodal biomedical AI (2022) · cites this paper as [41]

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Authors: Lim, B., Arık, S. Ö., Loeff, N., & Pfister, T.
Affiliations: University of Oxford (GB)
Google (United States) (US)
Corresponding author: Lim, Bryan
ORCID: Lim, Bryan
Arık, Sercan Ö.
Venue: International Journal of Forecasting
Publisher: Elsevier BV
Published: 2021-10
Volume: 37
Issue: 4
Pages: 1748-1764
ISSN: 0169-2070 (ISSN-L)
DOI: 10.1016/j.ijforecast.2021.03.012
OpenAlex: W2996331899
Cited by: 3,291
References: 41
Citations by year: 2026: 1359 · 2025: 827 · 2024: 508 · 2023: 362 · 2022: 153 · 2021: 66 · 2020: 16
FWCI: 126.76
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
creativecommons.org/licenses/by/4.0/ (vor)
Locations: International Journal of Forecasting (publishedVersion, cc-by)
arXiv (Cornell University) (submittedVersion)
RePEc: Research Papers in Economics (submittedVersion)
arXiv (Cornell University)
Funders: Google Inc
Topics: Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
Physical Sciences › Engineering › Building and Construction › Traffic Prediction and Management Techniques
Keywords: Temporal Fusion Transformer, interpretability, self-attention, gating mechanism, feature selection, exogenous variables, recurrent layers, long-range dependencies, temporal dynamics

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Temporal Fusion Transformers for interpretable multi-horizon time series forecasting

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