Selective Inference for Changepoint detection by Recurrent Neural
  Network
Tomohiro Shiraishi Daiki Miwa Vo Nguyen Le Duy Ichiro Takeuchi
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.