Automatic Network Structure-based Clustering of Multivariate Time Series

Obata, Kohei

松原, 靖子

Matsubara, Yasuko

川畑, 光希

Kawabata, Koki

中村, 航大

Nakamura, Kota

櫻井, 保志

Sakurai, Yasushi

Information Processing Society of Japan

ネットワーク構造

グラフィカルラッソ

time series

network structure

graphical lasso

In this paper we present NGL, pattern mining algorithm for multiple time series data with underlying network structures. Our method has the following properties: (a) Interpretable: it provides interpretable network structures for the data; (b) Automatic: it determines the optimal cut points and the number of clusters automatically; (c) Accurate: it provides reliable clustering performance thanks to the automated algorithm. We evaluate our NGL algorithm on synthetic datasets, outperforming state-of-the-art baselines in terms of accuracy. And extensive experiments on real datasets demonstrate that NGL does indeed obtain interpretable network structure clusters.

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松原, 靖子

川畑, 光希

中村, 航大

櫻井, 保志