Modeling long-range dependence, nonlinearity, and periodic phenomena in sea surface temperatures using tsmars

Document Type

Article

Publication Date

9-1-1997

Abstract

We analyze a time series of 20 years of daily sea surface temperatures measured off the California coast. The temperatures exhibit quite complicated features, such as effects on many different time scales, nonlinear effects, and long-range dependence. We show how a time series version of the multivariate adaptive regression splines (MARS) algorithm, TSMARS, can be used to obtain univariate adaptive spline threshold autoregressive models that capture many of the physical characteristics of the temperatures and are useful for short- and long-term prediction. We also discuss practical modeling issues, such as handling cycles, long-range dependence, and concurrent predictor time series using TSMARS. Models for the temperatures are evaluated using out-of-sample forecast comparisons, residual diagnostics, model skeletons, and sample functions of simulated series. We show that a categorical seasonal indicator variable can be used to model nonlinear structure in the data that is changing with time of year, but find that none of the models captures all of the cycles apparent in the data. © 1997 Taylor & Francis Group, LLC.

Identifier

21744456551 (Scopus)

Publication Title

Journal of the American Statistical Association

External Full Text Location

https://doi.org/10.1080/01621459.1997.10474043

e-ISSN

1537274X

ISSN

01621459

First Page

881

Last Page

893

Issue

439

Volume

92

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