Peng, Jyh-Ying and Aston, John A. D. (2011) The state space models toolbox for MATLAB. Journal of Statistical Software, Vol.41 (No.6). pp. 1-26.

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Abstract

State Space Models (SSM) is a MATLAB toolbox for time series analysis by state space methods. The software features fully interactive construction and combination of models, with support for univariate and multivariate models, complex time-varying (dynamic) models, non-Gaussian models, and various standard models such as ARIMA and structural time-series models. The software includes standard functions for Kalman filtering and smoothing, simulation smoothing, likelihood evaluation, parameter estimation, signal extraction and forecasting, with incorporation of exact initialization for filters and smoothers, and support for missing observations and multiple time series input with common analysis structure. The software also includes implementations of TRAMO model selection and Hillmer-Tiao decomposition for ARIMA models. The software will provide a general toolbox for time series analysis on the MATLAB platform, allowing users to take advantage of its readily available graph plotting and general matrix computation capabilities.

Item Type:	Journal Article
Subjects:	Q Science > QA Mathematics
Divisions:	Faculty of Science > Statistics
Library of Congress Subject Headings (LCSH):	MATLAB, State-space methods -- Computer programs, Time-series analysis -- Computer programs
Journal or Publication Title:	Journal of Statistical Software
Publisher:	University of California, Los Angeles
ISSN:	1548-7660
Official Date:	May 2011
Dates:	Date Event May 2011 Published
Date of first compliant deposit:	19 December 2015
Volume:	Vol.41
Number:	No.6
Page Range:	pp. 1-26
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access
Funder:	Guo jia ke xue wei yuan hui [National Science Council (Taiwan)], Engineering and Physical Sciences Research Council (EPSRC), Higher Education Funding Council for England (HEFCE)
Grant number:	94-2118-M-001-014 (NSC), 95-2118-M-001-003 (NSC), EP/H016856/1 (EPSRC)

Data sourced from Thomson Reuters' Web of Knowledge

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