摘要翻译：
状态空间模型(SSM)是MATLAB7.0软件工具箱，用于用状态空间方法进行时间序列分析。该软件支持单变量和多变量模型、复杂时变（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Kalman滤波和平滑、模拟平滑、似然估计、参数估计、信号提取和预测的标准功能，并结合滤波器和平滑器的精确初始化，支持缺失观测和具有共同分析结构的多个时间序列输入。该软件还包括TRAMO模型选择和ARIMA模型Hillmer-Tiao分解的实现。该软件将为在MATLAB平台上进行时间序列分析提供一个通用工具箱，使用户能够利用其现成的图形绘制和通用矩阵计算能力。
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英文标题：
《The SSM Toolbox for Matlab》
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作者：
Jyh-Ying Peng, John A. D. Aston
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最新提交年份：
2007
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分类信息：

一级分类：Statistics        统计学
二级分类：Computation        计算
分类描述：Algorithms, Simulation, Visualization
算法、模拟、可视化
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一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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英文摘要：
  State Space Models (SSM) is a MATLAB 7.0 software toolbox for doing 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 doing time series analysis on the MATLAB platform, allowing users to take advantage of its readily available graph plotting and general matrix computation capabilities.
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PDF链接：
https://arxiv.org/pdf/706.3443