Selective Inference for Changepoint detection by Recurrent Neural Network
This work addresses the risk of false detections in time series analysis for researchers and practitioners, offering a theoretically grounded approach to improve reliability, though it is incremental as it applies an existing framework to a specific method.
The study tackled the problem of false change point detections in time series using RNNs by introducing a selective inference method to provide statistically valid p-values, demonstrating its effectiveness through experiments on artificial and real data.
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.