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The widespread use of Artificial Intelligence (AI) has highlighted the importance of understanding AI model behavior. This understanding is crucial for practical decision-making, assessing model reliability, and ensuring trustworthiness. Interpreting time series forecasting models faces unique challenges compared to image and text data. These challenges arise from the temporal dependencies between time steps and the evolving importance of input features over time. My thesis focuses on addressing these challenges by aiming for more precise explanations of feature interactions, uncovering spatiotemporal patterns, and demonstrating the practical applicability of these interpretability techniques using real-world datasets and state-of-the-art deep learning models.
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This content will become publicly available on March 4, 2026
A Windowed Temporal Saliency Rescaling Method for Interpreting Time Series Deep Learning Models
Interpreting complex time series forecasting models is challenging due to the temporal dependencies between time steps and the dynamic relevance of input features over time. Existing interpretation methods are limited by focusing mostly on classification tasks, evaluating using custom baseline models instead of the latest time series models, using simple synthetic datasets, and requiring training another model. We introduce a novel interpretation method, Windowed Temporal Saliency Rescaling (WinTSR) addressing these limitations. WinTSR explicitly captures temporal dependencies among the past time steps and efficiently scales the feature importance with this time importance. We benchmark WinTSR against 10 recent interpretation techniques with 5 state-of-the-art deep-learning models of different architectures, including a time series foundation model. We use 3 real-world datasets for both time-series classification and regression. Our comprehensive analysis shows that WinTSR significantly outranks the other local interpretation methods in overall performance. Finally, we provide a novel and open-source framework to interpret the latest time series transformers and foundation models.
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- Award ID(s):
- 2151597
- PAR ID:
- 10583568
- Editor(s):
- Song, Dongjin; Xie, Yao; Purushotham, Sanjay; Chen, Haifeng; Shen, Cong
- Publisher / Repository:
- AAAI'25 Workshop: AI for Time Series Analysis: Theory, Algorithms, and Applications (AI4TS)
- Date Published:
- Edition / Version:
- 1
- Volume:
- 1
- Issue:
- 1
- Page Range / eLocation ID:
- https://github.com/AI4TS/AI4TS.github.io/blob/main/Camera_Ready_AAAI2025/21%5CCameraReady%5CWTSR_AAAI_2025.pdf
- Subject(s) / Keyword(s):
- Interpreting Windowed Temporal Saliency, Deep Learning
- Format(s):
- Medium: X Size: 848KB Other: pdf
- Size(s):
- 848KB
- Sponsoring Org:
- National Science Foundation
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