[3E4-GS-2] Machine learning: time-series data

[3E4-GS-2-04] Evaluating Out-of-Distribution Detection Using Deep-Learning Based Methods on Time-Series Data

〇Daichi Kimura¹, Tomonori Izumitani¹, Kenichiro Shimada¹, Kenji Kashima² (1. NTT communications, 2. Kyoto University)

Keywords:Time series, Out-of-Distribution detection, Generative model

It is necessary to detect the out-of-distribution of the time-series data because the difference in the distribution of the data between training and operation may affect the estimation results.AutoEncoder is one of the most well known methods for out-of-distribution detection. However, in recent years, it has been reported that AutoEncoder-based method often fails due to undesirable reconstruction of the out-of-distribution data in experiments using images. To deal with this problem, many generative model-based approaches using adversarial generative models have been proposed.Most of these methods have been performed on image data, and the performance of out-of-distribution detection on time-series sensor data is not fully explored. In this study, we evaluate and discuss the performance of the method on artificially generated data and real time series data.