Nonlinear modelling of periodic threshold autoregressions using TSMARS
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Authors
Lewis, Peter A.W.
Ray, Bonnie K.
Subjects
Nonlinearity
Periodic correlation
streamflows
threshold autoregression
TSMARS
Periodic correlation
streamflows
threshold autoregression
TSMARS
Advisors
Date of Issue
1997-09
Date
1997-09
Publisher
Language
Abstract
We present new methods for modeling nonlinear threshold-type autoregressive behaviour in periodically correlated time series. The methods are illustrated using a series of average monthly flows of the Fraser River in British Columbia. Commonly used nonlinearity tests of the river flow data in each month indicate nonlinear behaviour in certain months. The periodic nonlinear correlation structure is modelled nonparametrically using TSMARS, a time series version of Friedman's extended multivariate adaptive regression splines (MARS) algorithm, which allows for categorical predictor variables. We discuss two methods of using the computational algorithm in TSMARS for modeling and fitting periodically correlated data. The first method applies to the algorithm to data from each period separately. The second method models data from all periods simultaneously by incorporating an additional predictor variable to distinguish different behaviour in different periods, and allows for coalescing of data from periods with similar behaviour. The models obtained using TSMARS provde better short-term forecasts for the Fraser River data than a corresponding linear periodical AR model.
Type
Article
Description
Series/Report No
Department
Operations Research
Organization
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NPS Report Number
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Format
Citation
Journal of Time Series Analysis, Vol. 23, No. 4, 1997
Distribution Statement
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This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.