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超高次元時系列における予測および情報抽出の方法

超高维时间序列预测与信息提取方法

基本信息

批准号：
14380127
负责人：
KITAGAWA Genshiro
金额：
$ 8.13万
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：
2002
资助国家：
日本
起止时间：
2002 至 2005
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-14380127/
关键词：
high-dimensional time series multivariate AR model state-space model knowledge discovery sequential computation generalized information criterion power contribution prediction 一般化情報量基準

项目摘要

In the areas such as earth science, economics, finance, marketing, life science and environmental science, huge amount of data are being obtained. To develop tools for the extraction of useful information from these massive data, we performed research on the computational methods for fitting multivariate time series with very high-dimension, various sequential filtering algorithms and a method of detecting cosal relation between variables based on estimated multivariate time series model. The methods were applied various problems in real word. The major outcomes are as follows :1.Development of methods for fitting high-dimensional AR model.By using forward and backward prediction error sequences, a very efficient method for estimating AR coefficient matrices was developed. An algorithm for efficient computation on parallel processor is also developed.1.Filtering algorithms for high-dimensional state-space modelTo develop an efficient filtering method that can be applied very-dimensiona … More l state-space models, various algorithms based on information matrices, square-root algorithm, innovation type algorithm and approximation methods were considered. For the extension to nonlinear non-Gaussian state-space models, a new method of performing Gaussian-mixture approximation is also developed.2.Parallel Monte Carlo filter was developed for efficient sequential Monte Carlo filtering for complex problems based on parallel execution of many MCF. By numerical experiments, it was shown that the developed algorithm is very suitable for parallel computation and still maintains equivalent accuracy.3.Applications to real-world problems(1)A method of computing generalized power contribution from estimated AR model was modified and applied to various data sets such as electric power plant data and CDS (credit default swap) data and obtained useful information.(2)By the modeling from high-dimensional time series obtained from ocean bottom seismograph array, analysis method for underground structure was developed. Less

在地球科学、经济学、金融学、市场营销学、生命科学和环境科学等领域，正在获得大量的数据。为了开发从这些海量数据中提取有用信息的工具，我们研究了高维多元时间序列拟合的计算方法、各种序贯滤波算法以及基于估计的多元时间序列模型检测变量之间的cosal关系的方法。将该方法应用于真实的世界中的各种问题。主要研究成果如下：1.高维AR模型拟合方法的研究利用前向和后向预测误差序列，提出了一种非常有效的AR系数矩阵估计方法。1.高维状态空间模型的滤波算法提出了一种适用于高维状态空间模型的有效滤波方法 ...更多信息 l状态空间模型，考虑了基于信息矩阵的各种算法、平方根算法、新息型算法和近似方法。为了扩展到非线性非高斯状态空间模型，本文还提出了一种新的高斯混合近似方法。2.提出了并行蒙特卡罗滤波器，通过并行执行多个蒙特卡罗滤波器，实现了对复杂问题的高效顺序蒙特卡罗滤波。数值实验表明，所提出的算法非常适合并行计算，且仍能保持相当的精度。3.应用于实际问题（1）改进了一种由AR模型估计值计算广义功率贡献的方法，并将其应用于电厂数据和CDS（信用违约互换）数据等多种数据集，得到了有用的信息。(2)By利用海底地震台阵的高维时间序列建模，建立了地下结构分析方法。少