Haar wavelet-based technique for sharp jumps classification

A wavelet-based technique is proposed for analysing localized significant changes in observed data, in the presence of noise. The main tasks of the proposed technique are 1. noising the observed data without removing localized significant changes, assifying the detected sharp jumps (spikes), and taining a smooth trend (deterministic function) to represent the time-series evolution. ng 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, therefore, their analysis gives rise to multi-dimensional thresholds and constraints which allow both the denoising and the sorting of data in a suitable space.