AGCD： a robust periodicity analysis method based on approximate greatest common divisor

作     者：Juan YU Pei-zhong LU 

作者机构：School of Computer Science and Technology Fudan UniversiLy Shanghai 200433 China 

基　　金：Project supported by the National Natural Science Foundation of China (No. 60673082) 

出 版 物：《Frontiers of Information Technology & Electronic Engineering》 (信息与电子工程前沿（英文版）)

年 卷 期：2015年第16卷第6期

页      码：466-473页

摘      要：Periodicity is one of the most common phenomena in the physical world. The problem of periodicity analysis （or period detection） is a research topic in several areas, such as signal processing and data mining. However, period detection is a very challenging problem, due to the sparsity and noisiness of observational datasets of periodic events. This paper focuses on the problem of period detection from sparse and noisy observational datasets. To solve the problem, a novel method based on the approximate greatest common divisor （AGCD） is proposed. The proposed method is robust to sparseness and noise, and is efficient. Moreover, unlike most existing methods, it does not need prior knowledge of the rough range of the period. To evaluate the accuracy and efficiency of the proposed method, comprehensive experiments on synthetic data are conducted. Experimental results show that our method can yield highly accurate results with small datasets, is more robust to sparseness and noise, and is less sensitive to the magnitude of period than compared methods.

主 题 词：Periodicity analysis Period detection Sparsity Noise Approximate greatest common divisor (AGCD) 

学科分类：08[工学] 081202[081202] 0812[工学-测绘类] 

核心收录：

D　O　I：10.1631/FITEE.1400345

馆 藏 号：203956504...