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Validation, Robustness, and Accuracy of Perturbation-Based Sensitivity Analysis Methods for Time-Series Deep Learning Models
This work undertakes studies to evaluate Interpretability Methods for
Time-Series Deep Learning. Sensitivity analysis assesses how input changes
affect the output, constituting a key component of interpretation. Among the
post-hoc interpretation methods such as back-propagation, perturbation, and
approximation, my work will investigate perturbation-based sensitivity Analysis
methods on modern Transformer models to benchmark their performances.
Specifically, my work answers three research questions: 1) Do different
sensitivity analysis (SA) methods yield comparable outputs and attribute
importance rankings? 2) Using the same sensitivity analysis method, do
different Deep Learning (DL) models impact the output of the sensitivity
analysis? 3) How well do the results from sensitivity analysis methods align
with the ground truth?
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