In this study, we investigate the quantification of the statistical reliability of detected change points (CPs) in time series using a Recurrent Neural Network (RNN). Thanks to its flexibility, RNN holds the potential to effectively identify CPs in time series characterized by complex dynamics. However, there is an increased risk of erroneously detecting random noise fluctuations as CPs. The primary goal of this study is to rigorously control the risk of false detections by providing theoretically valid p-values to the CPs detected by RNN. To achieve this, we introduce a novel method based on the framework of Selective Inference (SI). SI enables valid inferences by conditioning on the event of hypothesis selection, thus mitigating selection bias. In this study, we apply SI framework to RNN-based CP detection, where characterizing the complex process of RNN selecting CPs is our main technical challenge. We demonstrate the validity and effectiveness of the proposed method through artificial and real data experiments.

翻译：本研究探讨了使用循环神经网络（RNN）对时间序列中检测到的变点（CP）进行统计可靠性量化的问题。得益于其灵活性，RNN有潜力有效识别具有复杂动态特性的时间序列中的变点。然而，这将增加将随机噪声波动误检测为变点的风险。本研究的主要目标是通过为RNN检测到的变点提供理论上有效的p值，严格控制误检风险。为此，我们基于选择性推断（SI）框架提出了一种新颖方法。SI通过以假设选择事件为条件进行有效推断，从而减轻选择偏差。在本研究中，我们将SI框架应用于基于RNN的变点检测，其中刻画RNN选择变点的复杂过程是主要技术挑战。通过人工数据和真实数据实验，我们验证了所提方法的有效性与实用性。