The reliability of deep time series models is often compromised by their tendency to rely on confounding factors, which may lead to incorrect outputs. Our newly recorded, naturally confounded dataset named P2S from a real mechanical production line emphasizes this. To avoid "Clever-Hans" moments in time series, i.e., to mitigate confounders, we introduce the method Right on Time (RioT). RioT enables, for the first time, 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.

翻译：深度时间序列模型的可靠性常因其倾向于依赖混杂因素而受损，这可能导致错误的输出。我们新记录的、来自真实机械生产线的自然混杂数据集P2S突显了这一问题。为避免时间序列中的"聪明汉斯"现象（即缓解混杂因素），我们提出了方法Right on Time（RioT）。RioT首次实现了在时域和频域上与模型解释的交互。随后利用两个领域中解释的反馈来约束模型，使其远离标注的混杂因素。这种双域交互策略对于有效处理时间序列数据集中的混杂因素至关重要。我们通过实验证明，RioT能有效引导模型在P2S数据集以及流行的时间序列分类与预测数据集中远离错误的原因。