Abstract
There has been much attention in recent years to the problem of detecting mean changes in a piecewise constant time series. Often, methods assume that the noise can be taken to be independent, identically distributed (IID), which in practice may not be a reasonable assumption. There is comparatively little work studying the problem of mean changepoint detection in time series with non-trivial autocovariance structure. In this article, we propose a likelihood-based method using wavelets to detect changes in mean in time series that exhibit time-varying autocovariance. Our proposed technique is shown to work well for time series with a variety of error structures via a simulation study, and we demonstrate its effectiveness on two data examples arising in economics.
Original language | English |
---|---|
Article number | e351 |
Journal | Stat |
Volume | 10 |
Issue number | 1 |
Early online date | 15 Jan 2021 |
DOIs | |
Publication status | Published - 31 Dec 2021 |
Funding
E. T. McGonigle gratefully acknowledges financial support from the Numerical Algorithms Group and EPSRC.
Keywords
- changepoint
- wavelets
- locally stationary
- autocorrelation