Implementation of the continuous wavelet transform for digital time series analysis

1997 ◽  
Vol 68 (3) ◽  
pp. 1484-1494 ◽  
Author(s):  
D. Jordan ◽  
R. W. Miksad ◽  
E. J. Powers
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Time Series Analysis ◽  
Continuous Wavelet Transform ◽  
Continuous Wavelet ◽  
Series Analysis ◽  
Digital Time

scholarly journals Time-space characterization of droughts in the São Francisco river catchment using the Standard Precipitation Index and continuous wavelet transform

RBRH ◽  
2019 ◽  
Vol 24 ◽  
Author(s):  
Marcus Suassuna Santos ◽  
Veber Afonso Figueiredo Costa ◽  
Wilson dos Santos Fernandes ◽  
Rafael Pedrollo de Paes
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Large Scale ◽  
Continuous Wavelet ◽  
River Catchment ◽  
Time Space ◽  
L Moments ◽  
Space Characterization

ABSTRACT This paper focuses on time-space characterization of drought conditions in the São Francisco River catchment, on the basis of wavelet analysis of Standardized Precipitation Index (SPI) time series. In order to improve SPI estimation, the procedures for regional analysis with L-moments were employed for defining statistically homogeneous regions. The continuous wavelet transform was then utilized for extracting time-frequency information from the resulting SPI time series in a multiresolution framework and for investigating possible teleconnections of these signals with those obtained from samples of the large-scale climate indexes ENSO and PDO. The use of regional frequency analysis with L-moments resulted in improvements in the estimation of SPI time series. It was observed that by aggregating regional information more reliable estimates of low frequency rainfall amounts were obtained. The wavelet analysis of climate indexes suggests that the more extreme dry periods in the study area are observed when the cold phase of both ENSO and the PDO coincides. While not constituting a strict cause effect relationship, it was clear that the more extreme droughts are consistently observed in this situation. However, further investigation is necessary for identifying particularities in rainfall patterns that are not associated to large-scale climate anomalies.

scholarly journals Advantages Of A Time Series Analysis Using Wavelet Transform As Compared With A Fourier Analysis

2015 ◽  
Vol 23 (2) ◽  
pp. 30-36 ◽  
Author(s):  
Patrik Sleziak ◽  
Kamila Hlavčová ◽  
Ján Szolgay
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Time Series Analysis ◽  
Fourier Analysis ◽  
Stationary Time Series ◽  
Precipitation Time Series ◽  
Software Environment ◽  
R Software ◽  
Series Analysis ◽  
Average Annual Precipitation

Abstract The paper presents an analysis of changes in the structure of the average annual discharges, average annual air temperature, and average annual precipitation time series in Slovakia. Three time series with lengths of observation from 1961 to 2006 were analyzed. An introduction to spectral analysis with Fourier analysis (FA) is given. This method is used to determine significant periods of a time series. Later in this article a description of a wavelet transform (WT) is reviewed. This method is able to work with non-stationary time series and detect when significant periods are presented. Subsequently, models for the detection of potential changes in the structure of the time series analyzed were created with the aim of capturing changes in the cyclical components and the multiannual variability of the time series selected for Slovakia. Finally, some of the comparisons of the time series analyzed are discussed. The aim of the paper is to show the advantages of time series analysis using WT compared with FT. The results were processed in the R software environment.

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