High-Dimensional Changepoint Detection via a Geometrically Inspired Mapping

01/15/2020
by   Thomas Grundy, et al.
0

High-dimensional changepoint analysis is a growing area of research and has applications in a wide range of fields. The aim is to accurately and efficiently detect changepoints in time series data when both the number of time points and dimensions grow large. Existing methods typically aggregate or project the data to a smaller number of dimensions; usually one. We present a high-dimensional changepoint detection method that takes inspiration from geometry to map a high-dimensional time series to two dimensions. We show theoretically and through simulation that if the input series is Gaussian then the mappings preserve the Gaussianity of the data. Applying univariate changepoint detection methods to both mapped series allows the detection of changepoints that correspond to changes in the mean and variance of the original time series. We demonstrate that this approach outperforms the current state-of-the-art multivariate changepoint methods in terms of accuracy of detected changepoints and computational efficiency. We conclude with applications from genetics and finance.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
10/02/2020

Extreme-SAX: Extreme Points Based Symbolic Representation for Time Series Classification

Time series classification is an important problem in data mining with s...
research
10/28/2015

Linear-time Detection of Non-linear Changes in Massively High Dimensional Time Series

Change detection in multivariate time series has applications in many do...
research
01/18/2022

WATCH: Wasserstein Change Point Detection for High-Dimensional Time Series Data

Detecting relevant changes in dynamic time series data in a timely manne...
research
02/16/2022

HDC-MiniROCKET: Explicit Time Encoding in Time Series Classification with Hyperdimensional Computing

Classification of time series data is an important task for many applica...
research
11/06/2020

A computationally efficient, high-dimensional multiple changepoint procedure with application to global terrorism incidence

Detecting changepoints in datasets with many variates is a data science ...
research
05/11/2023

Band-Pass Filtering with High-Dimensional Time Series

The paper deals with the construction of a synthetic indicator of econom...
research
10/14/2022

An Empirical Evaluation of Multivariate Time Series Classification with Input Transformation across Different Dimensions

In current research, machine and deep learning solutions for the classif...

Please sign up or login with your details

Forgot password? Click here to reset