Time Series Modelling with MATLAB: the SSpace toolbox
SSpace is a MATLAB toolbox for State-Space modeling that provides the user with tools for linear Gaussian, nonlinear, and non-Gaussian systems with the most advanced and up-to-date features available in any State-Space framework. Great flexibility is achieved because each model is coded on a standar...
| Autores: | , , , |
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| Tipo de documento: | capítulo de livro |
| Data de publicação: | 2019 |
| País: | España |
| Recursos: | Universidad de Castilla-La Mancha |
| Repositório: | RUIdeRA. Repositorio Institucional de la UCLM |
| OAI Identifier: | oai:ruidera.uclm.es:10578/24059 |
| Acesso em linha: | https://doi.org/10.1007/978-3-030-26036-1_6 http://hdl.handle.net/10578/24059 |
| Access Level: | Acceso aberto |
| Palavra-chave: | MATLAB State Space systems Kalman filter Maximum likelihood Smoother algorithm |
| Resumo: | SSpace is a MATLAB toolbox for State-Space modeling that provides the user with tools for linear Gaussian, nonlinear, and non-Gaussian systems with the most advanced and up-to-date features available in any State-Space framework. Great flexibility is achieved because each model is coded on a standard MATLAB function, thence having absolute control on particular parameterizations, parameter constraints, time variation of parameters or variances, arbitrary nonlinear relations with inputs, time aggregation, nested models, system concatenation, etc. The toolbox may be used by specifying State-Space systems from scratch or by using ready-to-use templates for standard methods (like VARMAX, exponential smoothing, unobserved components, Dynamic Linear Regression, etc.). The toolbox is freely available via a public code repository with full documentation and help system. This chapter demonstrates the toolbox’s potential with several examples. |
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