Research on the Methodology of Information Extraction and Knowledge Discovery Based on Statistical Time Seeries Modeling

基于统计时间序列建模的信息抽取与知识发现方法研究

• 批准号: 12680321
• 负责人: KITAGAWA Genshiro
• 金额: $ 2.5万
• 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 (C)
• 财政年份: 2000
• 资助国家: 日本
• 起止时间: 2000 至 2001
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-12680321/
• 关键词: General state-space model; Self-organization; Information criteria; Multivariate time series; Discovery science; Parallel computation; 情報量基準

Research upon this grant has been concerned in the following four fields ; (1) new methodologies in statistical modeling, (2) knowledge discovery based on statistical modeling, (3) application to actual data analysis and (4) software development. As for (1), resampling scheme in Monte Carlo filter is improved to stabilize the automatic model estimation via self-organizing state-space model. Also, several new method are tried to alleviate numerical diffculties often encountered in estimation of fixed parameters. (2) New algorithm for estimating Bayesian network is proposed which is designed for cDNA micro array data analysis. Minute by minute stock index data is analyzed by time series models, principal component analysis and cluster analysis to explore the calendar effect. (3) Based on multivariate time series model for ocean bottom seismograph data, an approximate space-time smoothing algorithm is proposed to estimate underground structure. Large-scale field-aligned currents are exhaustively analyzed, and automatic identification procedure is proposed. (4) Programs to estimate univariate and multivariate AR models are parallelized. This was done by the parallelization of Householder transformation. Filtering and smoothing algorithm for general state-space models is also implemented only on an experimental basis.

本次资助的研究涉及以下四个领域： （1）统计建模的新方法，（2）基于统计建模的知识发现，（3）在实际数据分析中的应用以及（4）软件开发。对于（1），改进了蒙特卡罗滤波器中的重采样方案，以通过自组织状态空间模型稳定自动模型估计。此外，还尝试了几种新方法来缓解固定参数估计中经常遇到的数值困难。 (2)提出了针对cDNA微阵列数据分析设计的贝叶斯网络估计新算法。通过时间序列模型、主成分分析和聚类分析对每分钟股指数据进行分析，探讨日历效应。 (3)基于海底地震仪数据的多元时间序列模型，提出了一种近似时空平滑算法来估计地下结构。对大规模场对准电流进行了详尽的分析，并提出了自动识别程序。 (4) 估计单变量和多变量 AR 模型的程序是并行的。这是通过 Householder 转换的并行化来完成的。一般状态空间模型的滤波和平滑算法也仅在实验基础上实现。

项目成果

Ikoma, T., N.Ichimura, T.Higuchi, H.Maeda: "Particle Filter Based Method for Maneuvering Target Tracking"IEEE International Workshop on Intelligent Signal Processing. 3-8 (2001)

Ikoma, T.、N.Ichimura、T.Higuchi、H.Maeda：“基于粒子滤波器的机动目标跟踪方法”IEEE 国际智能信号处理研讨会。

Kitagawa, G., T. Takanami and N. atsumoto: "Signal Extraction Problems in Seismology"international Statistical Review. Vol.69, No.1. 129-152 (2001)

Kitakawa, G.、T. Takanami 和 N. atsumoto：“地震学中的信号提取问题”国际统计评论。

Higuchi, T. S.I. Ohtani, T. Uozumi and K. Yumoto: "Pi2 onset time determination with information criterion"Journal of Geophysical Research. (in press). (2002)

Higuchi、T. S.I. Ohtani、T. Uozumi 和 K. Yumoto：“用信息准则确定 Pi2 起始时间”地球物理研究杂志。

Tkahashi, A. and Sato, S.: "Monte Carlo Filtering Approach for Estimating the Term Structure of Interest Rates"Annals of The Institute of Statistical Mathematics. Vol.53, No.1. 50-62 (2001)

Tkahashi, A. 和 Sato, S.：“用于估计利率期限结构的蒙特卡罗过滤方法”统计数学研究所年鉴。

北川源四郎, 川崎能典: "時系列モデルによるインフレ率予測誤差の分析"日本銀行調査統計局Working Paper. 01-13 (2001)

Genshiro Kitakawa、Yoshinori Kawasaki：“使用时间序列模型分析通胀率预测误差”日本银行研究统计部工作论文 01-13 (2001)。