Robust method for periodicity detection and characterization of irregular cyclical series in terms of embedded periodic components

A method for periodicity detection is proposed where unlike available metho ds a periodic component is characterized in terms of three basic periodicit y attributes: the periodicity (or period length), the periodic pattern, and the scaling factors associated with the successive nearly repetitive segme nts. A scheme is proposed for subsequent successive detection and extractio n of such (hidden) periodic or nearly periodic components constituting an i rregular cyclical series. To our knowledge, the proposed decomposition is m uch more powerful in terms of information content and robustness than the p resently available tools based on Fourier decomposition. Through the analys is of a variety of natural, experimental, and simulated data series, it is shown that the features of the periodicity attributes of the embedded perio dic components can lead to a meaningful characterization of an irregular se ries in a new perspective.