時系列モデルによる大量データからの情報抽出 : 地下水位データの解析(発見科学)

抄録

For automatic extraction of essential information and discovery from massive time series, it is necessary to develop a method which is flexible enough to handle actual phenomena in real world. That can be achieved by the use of state space model, and it provides us with a unified and computationally efficient filtering method and treating missing observations. As an example of successful applications of the method, analysis of groundwater level data is shown. It is shown that various discoveries are obtained from massive and noisy time series.