Projects per year
Abstract
In time series analysis there is an extensive literature on hypothesis tests that attempt to distinguish a stationary time series from a non-stationary one. However, the binary distinction provided by a hypothesis test can be somewhat blunt when trying to determine the degree of non-stationarity of a time series. This article creates an index that estimates a degree of non-stationarity. This index might be used, for example, to classify or discriminate between series. Our index is based on measuring the roughness of a statistic estimated from the time series, which is calculated from the roughness penalty associated with a spline smoothing/penalized least-squares method. We further use a resampling technique to obtain a likely range of index values obtained from a single realization of a time series. We apply our method to ascertain and compare the non-stationarity index of the well-known earthquake and explosion data.
Original language | English |
---|---|
Pages (from-to) | 295-305 |
Number of pages | 11 |
Journal | Stat |
Volume | 5 |
Issue number | 1 |
Early online date | 13 Nov 2016 |
DOIs | |
Publication status | Published - Feb 2017 |
Keywords
- time series
- non-parametric regression
- bootstrap assessment
Fingerprint
Dive into the research topics of 'Measuring the degree of non-stationarity of a time series'. Together they form a unique fingerprint.Projects
- 1 Finished
-
LuSTruM: Locally Stationary Time Series and Multiscale Methods for Statistics
Nason, G. P.
1/04/13 → 31/03/18
Project: Research