Development of Knowledge Discovery Systems by using the Hierarchical Bayesian Time Series Models

在这个项目中，我们处理了人工噪声的去除，这是一个绊脚石，在努力执行一个自动程序的知识发现从一个大规模的时间序列数据。更具体地说，我们专注于排除由于观察仪器的灵敏度变化而导致的趋势（背景平均值）的快速变化以及识别异常值的问题。层次贝叶斯模型中的自组织状态空间模型被用来解决这些问题。即使时间序列中的噪声分量显示出时间依赖性结构，它也能够估计趋势分量;例如，其方差取决于时间。我们开发的程序允许我们自动检测大规模时间序列数据集的依赖于调的平均结构。我们希望这个程序能为我们重新分析大量积累的数据集打开一扇大门，这些数据集由于各种噪声的明显信号污染而没有被详细检查。我们已经在Web.http://tswww.ism.ac.jp/higuchi/index e/Soft/index.htm上发布了这个程序，并附有用法说明

In this project, we dealt with a removal of the artificial noises that is a stumbling block in the effort to perform an automatic procedure for knowledge discovery from a large-scale time series data. More specifically, we focused on the problems to exclude a rapid change in a trend (background mean) due to changes in sensitivity of the observation instruments, and to identify an outlier. The self-organizing state space model which belongs to the hierarchical Bayesian model has been employed to solve these problems. It is capable of estimating the trend component even if the noise component in a time series shows a time-dependent structure ; ex., its variance depends on time. The program that we developed allows us to detect the tune-dependent mean structure automatically for a large-scale time series datasets. We hope this program will open a door for us to re-analyze huge accumulated dataset that has not been examined in detail owing to an apparent signal contamination by various noises. We have already post this program with an explanation for usage on Web.http://tswww.ism.ac.jp/higuchi/index e/Soft/index.htm