Andere Kunden interessierten sich auch für
![Elements of Nonlinear Time Series Analysis and Forecasting]( "Elements of Nonlinear Time Series Analysis and Forecasting")
Elements of Nonlinear Time Series Analysis and Forecasting133,99 €![An Introduction to Univariate and Multiple Time Series Analysis with R]( "An Introduction to Univariate and Multiple Time Series Analysis with R")
An Introduction to Univariate and Multiple Time Series Analysis with R40,99 €![Spectral Analysis for Univariate Time Series]( "Spectral Analysis for Univariate Time Series")
Spectral Analysis for Univariate Time Series93,99 €![Statistical Methods for Dynamic Models with Application]( "Statistical Methods for Dynamic Models with Application")
Statistical Methods for Dynamic Models with Application32,99 €![Time Series Forecasting Using Statistical And Neural Networks Models]( "Time Series Forecasting Using Statistical And Neural Networks Models")
Time Series Forecasting Using Statistical And Neural Networks Models32,99 €![UNIVARIATE AND BIVARIATE GAMMA-TYPE DISTRIBUTIONS]( "UNIVARIATE AND BIVARIATE GAMMA-TYPE DISTRIBUTIONS")
UNIVARIATE AND BIVARIATE GAMMA-TYPE DISTRIBUTIONS32,99 €![Time Series Analysis]( "Time Series Analysis")
Time Series Analysis45,99 €
This monograph examines nonlinear threshold time series models using TSMARS, a time series extension of the Multivariate Adaptive Regression Splines (MARS). MARS is model free and can detect and measure linear and curvilinear structure in data. Novel aspects include applications to Ireland's Trade Statistics and the introduction of regime dependent threshold seasonal time series models - the effect of seasonal adjustment in the presenence of a threshold is examined using these models. Two important new advances are incorporated into TSMARS. The first allows TSMARS to automatically treat ordinary and dynamic outliers. The second is a new procedure to estimate treshold moving average models within TSMARS. Both of these advances are described, implemented in SAS/IML, tested and results are reported. Finally, parametric and nonparametric bootstrapped procedures are described and the forecasts investigated.