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.

我们引入了Right on Time (RioT)方法，通过对模型在时域和频域上的解释的反馈来约束模型，使其远离注释的混淆因素，从而解决时序数据中混淆因素的困难问题，并在 P2S 数据集以及常见的时序分类和预测数据集中进行了有效的实证验证。

准时革新：通过限制解释来修订时间序列模型