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Research on Systemization of Time Series Analysis Software

时间序列分析软件系统化研究

基本信息

批准号：
08558021
负责人：
KITAGAWA Genshiro
金额：
$ 5.57万
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 (A)
财政年份：
1996
资助国家：
日本
起止时间：
1996 至 1998
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-08558021/
关键词：
Time Series Analysis State space model Nonlinear Non-Gaussian model Kalman filter Monte Carlo Filter Smoothing Sofware for Web 非定常時系列非線形モデルベイズモデルモデリング AIC 非ガウス型フィルタ

项目摘要

The objective of the research was, based on the recent progress of computer capabilities, to systemize the environment for advanced time series analysis with various models, algorithms, computational methods and softwares. For this purpose, we performed the followings :(1)Development of Generic Time Series Model and Related Computational Methods.We developed a time series analysis method based on general state space model which can be applied to very wide class of nonstationary nonlinear or non-Gaussian time series models. We published many papers related to this subjects.(2)Research on New Information CriteriaWe investigated new information criteria EIC and GIC.EIC is based on bootstrap bias correction for the log-likelihood, On the other hand, GIG evaluates the bias for any estimators defined by statistical functionals, By these two information criteria, it becomes possible to evaluate and compare models whose parameter are estiamted by various methods. Further, we refined the bias correction by GIC and developed a improved version of the generalized information criterion.(3)Development of Interfaces for Organization of SoftwaresRecent development of computer networks such as internet makes it easy to distribute or access to newly developed time series analysis softwares, In this research, Sato developed a unified method based on the Web. In this method, since all of the computations are performed within the server computer, user can always use these sofiwared only if they have access to internet and brouwser, In this research, he forcus on the seasonal adjustment program DECOMP, On the other hand, Ishiguro developed a software for the analysis of multivariate system based on multivariate AR model.

研究的目的是，根据计算机能力的最新进展，系统化的环境，先进的时间序列分析与各种模型，算法，计算方法和软件。（1）一般时间序列模型及相关计算方法的发展本文提出了一种基于一般状态空间模型的时间序列分析方法，该方法可以应用于非常广泛的一类非平稳、非线性或非高斯时间序列模型。我们发表了许多与这些主题相关的论文。（2）新信息准则的研究我们研究了新的信息准则EIC和GIC，EIC是基于对对数似然估计的Bootstrap偏差校正，GIG则是对由统计泛函定义的估计量的偏差进行估计，利用这两个信息准则，可以对参数估计方法不同的模型进行评价和比较。此外，我们改进了GIC的偏差校正，并开发了一个改进版本的广义信息准则。（3）软件组织的接口开发近年来，随着Internet等计算机网络的发展，新开发的时间序列分析软件的分发和访问变得容易。在本研究中，Sato开发了基于Web的统一方法。在这种方法中，由于所有的计算都是在服务器计算机上进行的，用户只有在访问互联网和浏览器时才能使用这些软件。在本研究中，他重点研究了季节调整程序DECOMP。另一方面，石黑浩开发了一个基于多元AR模型的多元系统分析软件。