Wavelet Transform Application for/in Non-Stationary Time-Series Analysis: A Review
Manouba University · Université de Sherbrooke · +4 more institutions
Abstract
Non-stationary time series (TS) analysis has gained an explosive interest over the recent decades in different applied sciences. In fact, several decomposition methods were developed in order to extract various components (e.g., seasonal, trend and abrupt components) from the non-stationary TS, which allows for an improved interpretation of the temporal variability. The wavelet transform (WT) has been successfully applied over an extraordinary range of fields in order to decompose the non-stationary TS into time-frequency domain. For this reason, the WT method is briefly introduced and reviewed in this paper. In addition, this latter includes different research and applications of the WT to non-stationary TS…
Citation impact
- FWCI
- 29.21
- Percentile
- 100%
- References
- 94
Authors
5- MRManel RhifCorresponding
Manouba University
- ABAli Ben Abbes
Université de Sherbrooke, Manouba University
- IRImed Riadh Farah
IMT Atlantique, Manouba University
- BMBeatriz Martínez
Universitat de València
- YSYan‐Fang Sang
Chinese Academy of Sciences, Institute of Geographic Sciences and Natural Resources Research
Topics & keywords
- Wavelet transform
- Wavelet
- Series (stratigraphy)
- Discrete wavelet transform
- Computer science
- Selection (genetic algorithm)
- Interpretation (philosophy)
- Explosive material
Funding
- NNNational Natural Science Foundation of ChinaAwards: 2017074, 2017YFA0603702, 91647110
- YIYouth Innovation Promotion Association of the Chinese Academy of SciencesAward: 2017074
- NKNational Key Research and Development Program of ChinaAwards: 2017074, 2017YFA0603702
- YIYouth Innovation Promotion AssociationAward: 2017074