基于二进制序列数据的确定事件周期性检测研究  

On Detecting Event Periodicity in Binary Data Series

作　　者：袁华[1] 杨锐[1] 钱宇[1] 罗谦[2] 

机构地区：[1]电子科技大学经济与管理学院,四川成都610054 [2]中国民用航空总局第二研究所,四川成都610041

出　　处：《管理工程学报》2015年第4期133-139,共7页Journal of Industrial Engineering and Engineering Management

基　　金：国家自然科学基金资助项目(71271044;71102055;U1233118);教育部高等学校博士学科点专项科研基金资助项目(2010018512002);国家科技支撑计划课题资助项目(2012BAG04B02)

摘　　要：特定事件在一段时间内的发生情况可以用二进制序列数据表示。为了探寻事件发生的周期性规律,本文提出了一种基于交叉熵的方法以有效检测二进制序列数据中给定事件的周期性。文章首先建立了对序列数据进行连续划分的π(n)方法;然后,基于π(n)划分结果分别定义了事件的分布(时间段)周期性和结构(时间点)周期性,并运用交叉熵来度量不同划分结果的优劣;最后,基于最小交叉熵原理和周期函数性质,提出了一个在离散数据中寻找事件周期的工程化方法。实验结果表明该方法可以有效检测事件在时间序列中的周期性并确定其周期值。Data series is defined as the series of data which present sequentially happening events. Real life has several examples of time-related data series, such as weather conditions of a particular location, stock trading data, transactions in a supermarket, computer network visiting traffic, gene sequencing data, etc. A time-related data series is mostly characterized by repeating cycles. The identification of periodic events or the detection of the periodicity of an event could shed light on the behavior habit and future trends of the case represented by time-related data series, hence leading to more effective decision making. Thus, the job of periodicity detection is a process for finding temporal regularities within the time-related data series, and the goal of analyzing a time series is to find whether and how frequent a periodic pattern（full or partial） is repeated within the series.Let et be the event occurring at timestamp t and S = {e1; e2;...; e｜S｜} be a time-related data series having ｜S｜ events, where et represents the event recorded at time instance i and ｜S｜ is the length of data series. In S, each event type can be denoted by a symbol（e.g., a, b, c）. In the following we will use x to denote a given event type. Specially, we can see two commonly facts in real world for event x = {＂Bob goes to gym＂}： ＂Bob goes to gym once a week ＂（not at any exact weekday.） and ＂Bob likes to go to gym on Tuesdays＂（not at every week.）. Actually, these two facts show some different periodicity of event x. The first addresses the fact that event x is averagely distributed in a week; whereas, the second one means event x will happened at a fixed time point（the distribution of x may not be so good）. Different from the traditional research, this study addresses the problem of how to detect these two periodicities for a given event x in data series S： First, we use the binary data series to present the appearances of a given event x in S by defining et =1 if x appears at time posi

关 键 词：二进制序列数据 周期事件 交叉熵 分布周期性 结构周期性 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]