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Title: Temporal Dependencies and Spatio-Temporal Patterns of Time Series Models
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.  more » « less
Award ID(s):
2151597
PAR ID:
10583569
Author(s) / Creator(s):
Corporate Creator(s):
Publisher / Repository:
Proceedings of the AAAI Conference on Artificial Intelligence, 38(21), 23391-23392.
Date Published:
Journal Name:
Proceedings of the AAAI Conference on Artificial Intelligence
Edition / Version:
1
Volume:
38
Issue:
21
ISSN:
2159-5399
Page Range / eLocation ID:
23391 to 23392
Subject(s) / Keyword(s):
Deep Learning, Interpretation, Time Series, Spatio-temporal, Explainability
Format(s):
Medium: X Size: 235KB Other: pdf
Size(s):
235KB
Sponsoring Org:
National Science Foundation
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