小波
小波变换
计算机科学
算法
离散小波变换
时间序列
平稳小波变换
系列(地层学)
数据挖掘
数学
级联算法
地质学
连贯性(哲学赌博策略)
统计
统计物理学
人工智能
机器学习
物理
古生物学
作者
Aslak Grinsted,John C. Moore,Svetlana Jevrejeva
标识
DOI:10.5194/npg-11-561-2004
摘要
Abstract. Many scientists have made use of the wavelet method in analyzing time series, often using popular free software. However, at present there are no similar easy to use wavelet packages for analyzing two time series together. We discuss the cross wavelet transform and wavelet coherence for examining relationships in time frequency space between two time series. We demonstrate how phase angle statistics can be used to gain confidence in causal relationships and test mechanistic models of physical relationships between the time series. As an example of typical data where such analyses have proven useful, we apply the methods to the Arctic Oscillation index and the Baltic maximum sea ice extent record. Monte Carlo methods are used to assess the statistical significance against red noise backgrounds. A software package has been developed that allows users to perform the cross wavelet transform and wavelet coherence (www.pol.ac.uk/home/research/waveletcoherence/).
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