# State-domain Change Point Detection for Nonlinear Time Series Regression

## TL;DR

This paper introduces a nonparametric method for detecting and estimating change points in the state domain of nonlinear time series, extending traditional time domain approaches with kernel-based techniques.

## Contribution

It proposes a novel density-weighted anti-symmetric kernel method for change point detection in the state domain of nonlinear time series, including estimation of their number and locations.

## Key findings

- Theoretical validation of the detection and estimation procedures.
- Effective change point detection demonstrated on real data.
- Method outperforms existing approaches in nonlinear settings.

## Abstract

Change point detection in time series has attracted substantial interest, but most of the existing results have been focused on detecting change points in the time domain. This paper considers the situation where nonlinear time series have potential change points in the state domain. We apply a density-weighted anti-symmetric kernel function to the state domain and therefore propose a nonparametric procedure to test the existence of change points. When the existence of change points is affirmative, we further introduce an algorithm to estimate the number of change points together with their locations. Theoretical results of the proposed detection and estimation procedures are given and a real dataset is used to illustrate our methods.