Abstract
We present a comprehensive, detailed review of time-series data analysis, with emphasis on deep time-series clustering (DTSC), and a case study in the context of movement behavior clustering utilizing the deep clustering method. Specifically, we modified the DCAE architectures to suit time-series data at the time of our prior deep clustering work. Lately, several works have been carried out on deep clustering of time-series data. We also review these works and identify state-of-the-art, as well as present an outlook on this important field of DTSC from five important perspectives.
Highlights
It shows the performance of the clustering algorithm in different spaces, i.e., the original data space and the hidden space learned via non-linear mapping with both Deep auto-encoders (DAEs) and deep convolutional auto-encoder (DCAE)
Within the latent space of AE, the clustering algorithm benefits from the DAE, which allows it to deal with learned features rather than raw data
DCAE allows local capture of the salience of signals and the obtaining of the specific variance of signals at different scales, which helps the clustering algorithm deal with the more clustering-friendly representation. It shows that univariate representation of data in K-means and DAE lost information compared with the multivariate analysis in DCAE
Summary
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