著者
村上 祐子
出版者
一般社団法人 人工知能学会
雑誌
人工知能 (ISSN:21882266)
巻号頁・発行日
vol.34, no.2, pp.176-181, 2019-03-01 (Released:2020-09-29)
著者
北川 源四郎
出版者
一般社団法人 人工知能学会
雑誌
人工知能 (ISSN:21882266)
巻号頁・発行日
vol.16, no.2, pp.300-307, 2001-03-01 (Released:2020-09-29)

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 general state space model, and it provides us with a unified tool for analyzing complex time series.To apply these general state space models, development of practical filtering and smoothing algorithms is indispensable.In this article, the non-Gaussian filter/smooother, Monte Carlo filter/smoother and self-organizing state space model are shown.As applications of the method, problems of detecting sudden changes of the trend and nonlinear smoothing are shown.