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

通过使用状态空间模型，可以处理大多数常见的时间序列模型。基于似然度的状态估计、预测、平滑和参数估计等问题，给出了有效的递归滤波算法。在本计画的研究中，我们开发了一套以状态空间法为基础的时间序列分析软体。此外，我们开发了一个网络版本的软件，并将该方法应用于各种现实世界的问题。主要研究结果如下：1.滤波和参数估计方法的发展：我们改进了蒙特卡罗滤波和高斯和滤波算法，以有效地计算条件状态分布。提出了状态估计的并行算法，以及状态和未知参数同时估计的算法.开发了蒙特卡罗滤波和自组织滤波的通用软件。开发了趋势估计、随机波动率估计、趋势与波动率模型、功率贡献分析等具体应用软件。网络软件的应用软件在用户的网站上，而无需安装这些软件。3.开发的方法和软件被应用于各种现实世界的问题，如地震学，地磁学，金融，宏观经济，市场营销，人类行为，船舶工程，和其他各种复杂系统，并在预测，控制和知识发现方面取得了有益的成果。

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.