[4Xin1] Poster session 2

[4Xin1-77] A Study on Segmentation of Time Series Data Using Neural Process

〇Tomohiro Mimura¹, Shin Ishiguro¹, Takashi Suzuki¹, Akira Yamada¹ (1.NTT DOCOMO,INC.)

Keywords:Neural process, time-series data, segmentation

In order to understand time series data, it is necessary to divide the data into meaningful chunks. However, it is difficult to manually segment a large amount of data. In this study, we discuss a computational model for segmenting time series data using an unsupervised learning model. In this paper, we compare the accuracy of segmentation by applying the Neural Process, Attentive Neural Process, and Conditional Neural Process to a generative model using the Neural Processes and HDP-HSMM.