The State Space Models Toolbox for MATLAB
The State Space Models Toolbox for MATLAB
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DOI：
10.18637/jss.v041.i06
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发表时间：
2011-05
影响因子：
5.8
通讯作者：
Jyh-Ying Peng;J. Aston
中科院分区：
文献类型：
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作者：
Jyh-Ying Peng;J. Aston
状态空间模型（State Space Models，SSM）是一个MATLAB工具箱，用于通过状态空间方法进行时间序列分析。该软件具有完全交互式的模型构建和组合功能，支持单变量和多变量模型、复杂的时变（动态）模型、非高斯模型以及各种标准模型，如ARIMA和结构时间序列模型。该软件包括卡尔曼滤波和平滑、模拟平滑、似然估计、参数估计、信号提取和预报等标准功能，并具有滤波器和平滑器的精确初始化功能，以及对缺失观测值和多个时间序列输入的通用分析结构支持。该软件还包括TRAMO模型选择和ARIMA模型的Hillmer-Tiao分解的实现。该软件将提供一个在MATLAB平台上进行时间序列分析的通用工具箱，使用户能够利用其现成的绘图和一般矩阵计算能力。
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 (dy- namic) models, non-Gaussian models, and various standard models such as ARIMA and structural time-series models. The software includes standard functions for Kalman fil- tering 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 com- mon 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.