Subspace Change-Point Detection via Low-Rank Matrix Factorisation

10/08/2021
by   Euan Thomas McGonigle, et al.
0

Multivariate time series can often have a large number of dimensions, whether it is due to the vast amount of collected features or due to how the data sources are processed. Frequently, the main structure of the high-dimensional time series can be well represented by a lower dimensional subspace. As vast quantities of data are being collected over long periods of time, it is reasonable to assume that the underlying subspace structure would change over time. In this work, we propose a change-point detection method based on low-rank matrix factorisation that can detect multiple changes in the underlying subspace of a multivariate time series. Experimental results on both synthetic and real data sets demonstrate the effectiveness of our approach and its advantages against various state-of-the-art methods.

READ FULL TEXT
research
04/29/2023

Change point detection in low-rank VAR processes

Vector autoregressive (VAR) models are widely used in multivariate time ...
research
02/10/2018

Detecting Multiple Step Changes Using Adaptive Regression Splines with Application to Neural Recordings

Time series produced by dynamical systems as frequently the case in neur...
research
02/15/2021

Tight Risk Bound for High Dimensional Time Series Completion

Initially designed for independent datas, low-rank matrix completion was...
research
02/02/2020

Modeling Multivariate Spatial-Temporal Data with Latent Low-Dimensional Dynamics

High-dimensional multivariate spatial-temporal data arise frequently in ...
research
01/05/2016

Low-Rank Representation over the Manifold of Curves

In machine learning it is common to interpret each data point as a vecto...
research
03/16/2021

Soft and subspace robust multivariate rank tests based on entropy regularized optimal transport

In this paper, we extend the recently proposed multivariate rank energy ...
research
08/24/2012

Changepoint detection for high-dimensional time series with missing data

This paper describes a novel approach to change-point detection when the...

Please sign up or login with your details

Forgot password? Click here to reset