# Multiple Change Point Detection and Validation in Autoregressive Time Series Data

arXiv: https://arxiv.org/abs/1912.07775 · HTML: https://mlsanity.com/paper/1912.07775/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Dec 2019.

## Built on by (most cited first, 3 of 3)

- [Online Change Points Detection for Linear Dynamical Systems with Finite Sample Guarantees](https://mlsanity.com/paper/2311.18769/) (2023)
- [A Greedy Graph Search Algorithm Based on Changepoint Analysis for Automatic QRS Complex Detection](https://mlsanity.com/paper/2102.03538/) (2021)
- [Continuous Optimization for Offline Change Point Detection and Estimation](https://mlsanity.com/paper/2407.03383/) (2024)

## Abstract (arXiv)

It is quite common that the structure of a time series changes abruptly. Identifying these change points and describing the model structure in the segments between these change points is of interest. In this paper, time series data is modelled assuming each segment is an autoregressive time series with possibly different autoregressive parameters. This is achieved using two main steps. The first step is to use a likelihood ratio scan based estimation technique to identify these potential change points to segment the time series. Once these potential change points are identified, modified parametric spectral discrimination tests are used to validate the proposed segments. A numerical study is conducted to demonstrate the performance of the proposed method across various scenarios and compared against other contemporary techniques.