Corpus ID: 212414796

What went wrong and when? Instance-wise Feature Importance for Time-series Models

@article{Tonekaboni2020WhatWW,
  title={What went wrong and when? Instance-wise Feature Importance for Time-series Models},
  author={Sana Tonekaboni and S. Joshi and D. Duvenaud and Anna Goldenberg},
  journal={ArXiv},
  year={2020},
  volume={abs/2003.02821}
}
  • Sana Tonekaboni, S. Joshi, +1 author Anna Goldenberg
  • Published 2020
  • Computer Science, Mathematics
  • ArXiv
    • Explanations of time series models are useful for high stakes applications like healthcare but have received little attention in machine learning literature. We propose FIT, a framework that evaluates the importance of observations for a multivariate time-series black-box model, by quantifying the shift in the predictive distribution over time. FIT defines the importance of an observation based on its contribution to the distributional shift under a KL-divergence that contrasts the predictive… CONTINUE READING
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    5 Citations
    Background Citations
    2
    Methods Citations
    1
    5 Citations
    Citation Type

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    • 2
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