We introduce a new periodicity detection algorithm for binary time series of event onsets, the Gaussian Mixture Periodicity Detection Algorithm (GMPDA). The algorithm approaches the periodicity detection problem to infer the parameters of a generative model. We specified two models - the Clock and Random Walk - which describe two different periodic phenomena and provide a generative framework. The algorithm achieved strong results on test cases for single and multiple periodicity detection and varying noise levels. The performance of GMPDA was also evaluated on real data, recorded leg movements during sleep, where GMPDA was able to identify the expected periodicities despite high noise levels. The paper's key contributions are two new models for generating periodic event behavior and the GMPDA algorithm for multiple periodicity detection, which is highly accurate under noise.
翻译:我们为事件发端的二进制时间序列引入了新的周期检测算法,即高森混合物周期性测算法(GMPDA)。算法将周期检测问题作为周期检测问题来推断基因模型的参数。我们指定了两种模型,即时钟和随机行走(Clock and Random walk),描述两种不同的周期现象,并提供一个基因化框架。算法在单一和多周期检测的测试案例和不同噪音水平方面取得了显著结果。GMPDA的性能还根据真实数据进行了评估,记录了睡眠期间的腿动静,而GMPDA尽管噪音水平很高,仍能够识别预期的周期性。文件的主要贡献是产生定期事件行为的两个新模型和多次检测的GPDA算法,在噪音下非常准确。