Nonlinear modeling of time series using Multivariate Adaptive Regression Splines (MARS)
By: Peter A. W. Lewis and James G. Stevens
Contributors: Stevens, James G.
Publisher: Naval Postgraduate School
Published: 1990
Place of publication: Monterey, Calif
Language: eng
Number of pages: 34
Subjects: Regression analysis; Mathematical models; Time-series analysis; Multivariate analysis; Nonlinear theories
Other titles: NPS-55-90-10.
Description: MARS(Multivariate Adaptive Regression Splines). Abstract: MARS is a new methodology, due to Friedman, for nonlinear regression modeling. MARS can be conceptualized as a generalization of recursive partitioning that uses spline fitting in lieu of other simple functions. Given a set of predictor variables, MARS fits a model in a form of an expansion of product spline basis functions of predictors chosen during a forward and backward recursive partitioning strategy. MARS produces continuous models for discrete data that can have multiple partitions and multilinear terms. Predictor variable contributions and interactions in a MARS model may be analyzed using an ANOVA style decomposition. By letting the predictor variables in MARS be lagged values of a time series, one obtains a new method for nonlinear autoregressive threshold modeling of time series. A significant feature of this extension of MARS is its ability to produce models with limit cycles when modeling time series data that exhibit periodic behavior. In a physical context, limit cycles represent a stationary state of sustained oscillations, a satisfying behavior for any model of a time series with periodic behavior. Analysis of the Wolf sunspot numbers with MARS appears to give an improvement over existing nonlinear Threshold and Bilinear models.
Notes: "NPS-55-90-10."

Cover title.

"April 1990."

AD A222 710.

Includes bibliographical references (p. 25-26)