Dynamic Variable Dependency Encoding and Its Application on Change Point Detection

Hao Huang, Shinjae Yoo

Abstract: Multivariate time series usually have complex and time-varying dependencies among variables. In order to spot changes and interpret temporal dynamics, it is essential to understand these dependencies and how they evolve over time. However, the problem of acquiring and monitoring them is extremely challenging due to the dynamic and nonlinear interactions among time series. In this paper, we propose a dynamic dependency learning method, which learns dependency latent space with a two-level attention model. The first level is a bi-sided attention module to learn the short-term dependencies. Once the sequence of short-term dependencies is collected over a certain period of time, a temporal self-attention module is applied to obtain the actual dependencies for the current timestamp. The coordinates in latent space is descriptor of the temporal dynamic. We apply this descriptor to change point detection, and experiments show that our proposed method outperforms popular baselines.

External IDs:dblp:conf/pakdd/HuangY23