# Simultaneous multiple change-point and factor analysis for high-dimensional time series

arXiv: https://arxiv.org/abs/1612.06928 · HTML: https://mlsanity.com/paper/1612.06928/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Dec 2016.

## Built on by (most cited first, 5 of 5)

- [Causal Discovery-Driven Change Point Detection in Time Series](https://mlsanity.com/paper/2407.07290/) (2024)
- [Subspace Change-Point Detection via Low-Rank Matrix Factorisation](https://mlsanity.com/paper/2110.04044/) (2021)
- [Detection and Estimation of Structural Breaks in High-Dimensional Functional Time Series](https://mlsanity.com/paper/2304.07003/) (2023)
- [Modeling Systemic Risk: A Time-Varying Nonparametric Causal Inference Framework](https://mlsanity.com/paper/2312.16707/) (2023)
- [TimePred: efficient and interpretable offline change point detection for high volume data -- with application to industrial process monitoring](https://mlsanity.com/paper/2512.01562/) (2025)

## Abstract (arXiv)

We propose the first comprehensive treatment of high-dimensional time series factor models with multiple change-points in their second-order structure. We operate under the most flexible definition of piecewise stationarity, and estimate the number and locations of change-points consistently as well as identifying whether they originate in the common or idiosyncratic components. Through the use of wavelets, we transform the problem of change-point detection in the second-order structure of a high-dimensional time series, into the (relatively easier) problem of change-point detection in the means of high-dimensional panel data. Also, our methodology circumvents the difficult issue of the accurate estimation of the true number of factors in the presence of multiple change-points by adopting a screening procedure. We further show that consistent factor analysis is achieved over each segment defined by the change-points estimated by the proposed methodology. In extensive simulation studies, we observe that factor analysis prior to change-point detection improves the detectability of change-points, and identify and describe an interesting `spillover' effect in which substantial breaks in the idiosyncratic components get, naturally enough, identified as change-points in the common components, which prompts us to regard the corresponding change-points as also acting as a form of `factors'. Our methodology is implemented in the R package tt factorcpt, available from CRAN.