Temporal Saliency Detection Towards Explainable Transformer-based
Timeseries Forecasting
- URL: http://arxiv.org/abs/2212.07771v3
- Date: Fri, 15 Sep 2023 08:31:09 GMT
- Title: Temporal Saliency Detection Towards Explainable Transformer-based
Timeseries Forecasting
- Authors: Nghia Duong-Trung, Duc-Manh Nguyen, Danh Le-Phuoc
- Abstract summary: This paper introduces Temporal Saliency Detection (TSD), an effective approach that builds upon the attention mechanism and applies it to multi-horizon time series prediction.
The TSD approach facilitates the multiresolution analysis of saliency patterns by condensing multi-heads, thereby progressively enhancing the forecasting of complex time series data.
- Score: 3.046315755726937
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Despite the notable advancements in numerous Transformer-based models, the
task of long multi-horizon time series forecasting remains a persistent
challenge, especially towards explainability. Focusing on commonly used
saliency maps in explaining DNN in general, our quest is to build
attention-based architecture that can automatically encode saliency-related
temporal patterns by establishing connections with appropriate attention heads.
Hence, this paper introduces Temporal Saliency Detection (TSD), an effective
approach that builds upon the attention mechanism and applies it to
multi-horizon time series prediction. While our proposed architecture adheres
to the general encoder-decoder structure, it undergoes a significant renovation
in the encoder component, wherein we incorporate a series of information
contracting and expanding blocks inspired by the U-Net style architecture. The
TSD approach facilitates the multiresolution analysis of saliency patterns by
condensing multi-heads, thereby progressively enhancing the forecasting of
complex time series data. Empirical evaluations illustrate the superiority of
our proposed approach compared to other models across multiple standard
benchmark datasets in diverse far-horizon forecasting settings. The initial TSD
achieves substantial relative improvements of 31% and 46% over several models
in the context of multivariate and univariate prediction. We believe the
comprehensive investigations presented in this study will offer valuable
insights and benefits to future research endeavors.
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