01969nas a2200277 4500008004100000245008900041210006900130260001200199300001200211490000700223520116400230653001601394653003301410653003901443653002501482100001401507700001901521700001601540700001701556700001901573700001301592700001501605700001801620700002001638856003301658 2013 eng d00aMining Effective Multi-Segment Sliding Window for Pathogen Incidence Rate Prediction0 aMining Effective MultiSegment Sliding Window for Pathogen Incide c09/2013 a425-4440 v873 aPathogen incidence rate prediction, which can be considered as time series modeling, is an important task for infectious disease incidence rate prediction and for public health. This paper investigates applying a genetic computation technique, namely GEP, for pathogen incidence rate prediction. To overcome the shortcomings of traditional sliding windows in GEP based time series modeling, the paper introduces the problem of mining effective sliding window, for discovering optimal sliding windows for building accurate prediction models. To utilize the periodical characteristic of pathogen incidence rates, a multi-segment sliding window consisting of several segments from different periodical intervals is proposed and used. Since the number of such candidate windows is still very large, a heuristic method is designed for enumerating the candidate effective multi-segment sliding windows. Moreover, methods to find the optimal sliding window and then produce a mathematical model based on that window are proposed. A performance study on real-world datasets shows that the techniques are effective and efficient for pathogen incidence rate prediction.10aData Mining10aMulti-segment sliding window10aPathogen incidence rate prediction10aTime series modeling1 aDuan, Lei1 aTang, Changjie1 aLi, Xiasong1 aDong, Guozhu1 aWang, Xianming1 aZuo, Jie1 aJiang, Min1 aLi, Zhongqiao1 aZhang, Yongqing uhttp://knoesis.org/node/2462