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Development of Time Series Analysis Software Based on State-Space Modeling

基于状态空间建模的时间序列分析软件开发

基本信息

批准号：
13558025
负责人：
KITAGAWA Genshiro
金额：
$ 7.04万
依托单位：
The Institute of Statistical Mathematics
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (B)
财政年份：
2001
资助国家：
日本
起止时间：
2001 至 2004
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-13558025/
关键词：
State -space model Monte Carlo filter self organizing model parallel computation nonstationary time series time series analysis software Bayesian model non-Gaussian filter 時系列解析ソフトウェア超多変量時系列多変量ARモデル知識発見逐次計算アルゴリズム一般化情報量規準パ

项目摘要

By using the state-space model, most of the familiar time series models can be treated. It also provides efficient recursive filtering algorithm for state estimation, prediction, smoothing and parameter estimation based on the likelihood. In this project research, we developed a time series analysis software based on this state-space approach. Further, we developed a Web-version of the softwares and applied the method to various real-world problems. The major outcomes are as follows:1. Development of filtering and parameter estimation method.We refined the algorithms of the Monte Carlo filter and the Gaussian-sum filter for efficient computation of conditional state distributions. We also developed parallel algorithm for computing estimation of the state and some algorithms for simultaneous estimation of the state and the unknown parameters.2. We developed generic softwares for Monte Carlo filtering and self-organizing filter. Specific application softwares were also developed for trend estimation, estimation of stochastic volatility, trend and volatility model and power contribution analysis. Web-software for the application software on user's Web site without installing these softwares.3. The developed methods and softwares were applied to various real-world problems such as the seismology, earth-magnetography, finance, macro-economics, marketing, human behavior, ship engineering, and various other complex systems and obtained useful results in prediction, control and knowledge discovery.

利用状态空间模型可以处理大多数常见的时间序列模型。它还为状态估计、预测、平滑和基于似然的参数估计提供了高效的递归滤波算法。在本项目研究中，我们开发了一个基于该状态空间方法的时间序列分析软件。此外，我们开发了该软件的Web版本，并将该方法应用于各种现实世界的问题。主要研究成果如下：1.滤波和参数估计方法的发展：改进了蒙特卡罗滤波和高斯和滤波的算法，提高了条件状态分布的计算效率。给出了状态估计的并行算法和一些同时估计状态和未知参数的算法。我们开发了蒙特卡罗滤波和自组织滤波的通用软件。开发了用于趋势估计、随机波动率估计、趋势和波动率模型以及功率贡献分析的具体应用软件。Web-用于用户网站上的应用软件的软件，不安装这些软件。所开发的方法和软件被应用于地震学、地磁学、金融学、宏观经济学、市场营销、人类行为、船舶工程等各种复杂系统的实际问题，在预测、控制和知识发现方面取得了有用的结果。