On Detecting Feasible Periodicity for Periodic Event in Binary Data Series
نویسندگان
چکیده
In this paper, we investigated the problem of discovering periodicity of a certain event in a binary data series and a new method basing on cross entropy is proposed. First, a series of rational partition methods for binary data series are introduced, which can divide the data series into different segments (partition). Then, we use cross entropy to calculate the partition periodicity, which could be the good measurements for the feasible of event periodicity. Finally, a periodicity evaluation method is proposed to obtain the feasible periodicity of the given event. The results of calculation example show that the method can be used to explore feasible event periodicity in binary data series.
منابع مشابه
A highly specific test for periodicity.
We present a method that allows to distinguish between nearly periodic and strictly periodic time series. To this purpose, we employ a conservative criterion for periodicity, namely, that the time series can be interpolated by a periodic function whose local extrema are also present in the time series. Our method is intended for the analysis of time series generated by deterministic time-contin...
متن کاملEffective periodic pattern mining in time series databases
The goal of analyzing a time series database is to find whether and how frequent a periodic pattern is repeated within the series. Periodic pattern mining is the problem that regards temporal regularity. However, most of the existing algorithms have a major limitation in mining interesting patterns of users interest, that is, they can mine patterns of specific length with all the events sequent...
متن کاملOn the Detection of Trends in Time Series of Functional Data
A sequence of functions (curves) collected over time is called a functional time series. Functional time series analysis is one of the popular research areas in which statistics from such data are frequently observed. The main purpose of the functional time series is to predict and describe random mechanisms that resulted in generating the data. To do so, it is needed to decompose functional ti...
متن کاملOn Periodicity Detection and Structural Periodic Similarity
This work motivates the need for more flexible structural similarity measures between time-series sequences, which are based on the extraction of important periodic features. Specifically, we present non-parametric methods for accurate periodicity detection and we introduce new periodic distance measures for time-series sequences. The goal of these tools and techniques are to assist in detectin...
متن کاملMining Periodicity from Dynamic and Incomplete Spatiotemporal Data
As spatiotemporal data becomes widely available, mining and understanding such data have gained a lot of attention recently. Among all important patterns, periodicity is arguably the most frequently happening one for moving objects. Finding periodic behaviors is essential to understanding the activities of objects, and to predict future movements and detect anomalies in trajectories. However, p...
متن کامل