Temporal Dependencies and Spatio-Temporal Patterns of Time Series Models

Authors

  • Md. Khairul Islam University of Virginia

DOI:

https://doi.org/10.1609/aaai.v38i21.30396

Keywords:

Deep Learning, Interpretation, Time Series, Spatio-temporal, Explainability

Abstract

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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Published

2024-03-24

How to Cite

Islam, M. K. (2024). Temporal Dependencies and Spatio-Temporal Patterns of Time Series Models. Proceedings of the AAAI Conference on Artificial Intelligence, 38(21), 23391-23392. https://doi.org/10.1609/aaai.v38i21.30396