Right on Time: Revising Time Series Models by Constraining their Explanations
CoRR(2024)
摘要
The reliability of deep time series models is often compromised by their
tendency to rely on confounding factors, which may lead to misleading results.
Our newly recorded, naturally confounded dataset named P2S from a real
mechanical production line emphasizes this. To tackle the challenging problem
of mitigating confounders in time series data, we introduce Right on Time
(RioT). Our method enables interactions with model explanations across both the
time and frequency domain. Feedback on explanations in both domains is then
used to constrain the model, steering it away from the annotated confounding
factors. The dual-domain interaction strategy is crucial for effectively
addressing confounders in time series datasets. We empirically demonstrate that
RioT can effectively guide models away from the wrong reasons in P2S as well as
popular time series classification and forecasting datasets.
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