.png){kind=icon}
Return
Multiple Changepoint Detection Using Metadata
.png){kind=icon}
OA
AI
DOI：10.1175/JCLI-D-14-00442.1.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper examines multiple changepoint detection procedures that use station history (metadata) information. Metadata records are available for some climate time series; however, these records are notoriously incomplete and many station moves and gauge changes are unlisted (undocumented). The shift in a series must be comparatively larger to declare a changepoint at an undocumented time. Also, the statistical methods for the documented and undocumented scenarios radically differ: a simple t test adequately detects a single mean shift at a documented changepoint time, while a t(max) distribution is appropriate for a single undocumented changepoint analysis. Here, the multiple changepoint detection problem is considered via a Bayesian approach, with the metadata record being used to formulate a prior distribution of the changepoint numbers and their location times. This prior distribution is combined with the data to obtain a posterior distribution of changepoint numbers and location times. Estimates of the most likely number of changepoints and times are obtained from the posterior distribution. Simulation studies demonstrate the efficacy of this approach. The methods, which are applicable with or without a reference series, are applied in the analysis of an annual precipitation series from New Bedford, Massachusetts.
Keywords:
TIME-SERIES
MODEL
REGRESSION
LEVEL
BAYES
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4
Papers:
1.4W
Citations:
5.9W
Organization
Cited Papers
No cited papers available