A wavelet-based technique is proposed for analysing ocalized significant changes in observed data, in the presence of noise. The main tasks of the proposed technique are: (a) denoising the observed data without removing localized significant changes, (b) classifying the detected sharp jumps (sèikes), and (c) obtaining a smooth trend (deterministic function) to represent the time-series evolution. By using the Haar discrete wavelet transform, the sequence of data is transformed into a sequence of wavelet coefficients. The Haar wavelet coefficients together with their rate of change, represent local changes and local correlation of data, therfore, their analysis gives rise to multi-dimensional thresholds and constraints which allow both the denoising and the sorting of data in a suitable space.
Haar wavelet-based technique for sharp jumps classification
CATTANI, Carlo
2004-01-01
Abstract
A wavelet-based technique is proposed for analysing ocalized significant changes in observed data, in the presence of noise. The main tasks of the proposed technique are: (a) denoising the observed data without removing localized significant changes, (b) classifying the detected sharp jumps (sèikes), and (c) obtaining a smooth trend (deterministic function) to represent the time-series evolution. By using the Haar discrete wavelet transform, the sequence of data is transformed into a sequence of wavelet coefficients. The Haar wavelet coefficients together with their rate of change, represent local changes and local correlation of data, therfore, their analysis gives rise to multi-dimensional thresholds and constraints which allow both the denoising and the sorting of data in a suitable space.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
