scholarly journals SPATIOTEMPORAL ANALYSIS OF VARIATIONS OF CERTAIN MORPHOLOGICAL BEACH FORMS

2012 ◽  
Vol 1 (33) ◽  
pp. 82 ◽  
Author(s):  
Zbigniew Pruszak ◽  
Jan Schönhofer ◽  
Grzegorz Różyński
Keyword(s):  
Spectral Analysis ◽  
Wavelet Transform ◽  
Surf Zone ◽  
Spatial Scales ◽  
Spatiotemporal Analysis ◽  
Discrete Wavelet ◽  
Field Observations ◽  
Simple Geometry ◽  
Meso Scale

The study is focused on spatiotemporal shoreline variability at a beach with a wide surf zone featuring 3-5 bars based on field observations done between 1983-2008 in the south Baltic Sea. The implementation of various analyses from simple geometry, through spectral analysis up to Discrete Wavelet Transform (DWT) allowed for a synergistic description of simultaneous shoreline and dune foot variability in space and time. The results include meso-scale and long-term phenomena with time scales from several months to many years and spatial scales ranking between a few hundred meters up to several kilometers.

MULTIVARIATE SPECTRAL ANALYSIS USING HILBERT WAVELET PAIRS

2004 ◽  
Vol 02 (04) ◽  
pp. 567-587 ◽  
Author(s):  
BRANDON WHITCHER ◽  
PETER F. CRAIGMILE
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Multivariate Time Series ◽  
Discrete Wavelet ◽  
Pass Filter ◽  
Wavelet Filters ◽  
The Hilbert Transform ◽  
High Pass

We investigate the use of Hilbert wavelet pairs (HWPs) in the non-decimated discrete wavelet transform for the time-varying spectral analysis of multivariate time series. HWPs consist of two high-pass and two low-pass compactly supported filters, such that one high-pass filter is the Hilbert transform (approximately) of the other. Thus, common quantities in the spectral analysis of time series (e.g., power spectrum, coherence, phase) may be estimated in both time and frequency. Compact support of the wavelet filters ensures that the frequency axis will be partitioned dyadically as with the usual discrete wavelet transform. The proposed methodology is used to analyze a bivariate time series of zonal (u) and meridional (v) winds over Truk Island.

scholarly journals Exploring relationship between crude oil price volatility and stock indices movement using wavelet analysis: evidence from India and China

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shekhar Mishra ◽  
Sathya Swaroop Debasish
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Crude Oil ◽  
Equity Market ◽  
Oil Price ◽  
Discrete Wavelet ◽  
Crude Oil Price ◽  
Content Type ◽  
India And China

Purpose This study aims to explore the linkage between fluctuations in the global crude oil price and equity market in fast emerging economies of India and China. Design/methodology/approach The present research uses wavelet decomposition and maximal overlap discrete wavelet transform (MODWT), which decompose the time series into various frequencies of short, medium and long-term nature. The paper further uses continuous and cross wavelet transform to analyze the variance among the variables and wavelet coherence analysis and wavelet-based Granger causality analysis to examine the direction of causality between the variables. Findings The continuous wavelet transform indicates strong variance in WTIR (return series of West Texas Instrument crude oil price) in short, medium and long run at various time periods. The variance in CNX Nifty is observed in the short and medium run at various time periods. The Chinese stock index, i.e. SCIR, experiences very little variance in short run and significant variance in the long and medium run. The causality between the changes in crude oil price and CNX Nifty is insignificant and there exists a bi-directional causality between global crude oil price fluctuations and the Chinese equity market. Originality/value To the best of the authors’ knowledge, very limited work has been done where the researchers have analyzed the linkage between the equity market and crude oil price fluctuations under the framework of discrete wavelet transform, which overlooks the bottleneck of non-stationarity nature of the time series. To bridge this gap, the present research uses wavelet decomposition and MODWT, which decompose the time series into various frequencies of short, medium and long-term nature.

Spectral Analysis of Multichannel Meg Data

Fractals ◽  
1998 ◽  
Vol 06 (04) ◽  
pp. 395-400 ◽  
Author(s):  
Zhihau Chen ◽  
Alex Tretyakov ◽  
Hideki Takayasu ◽  
Nobukazu Nakasato
Keyword(s):  
Spectral Analysis ◽  
Wavelet Transform ◽  
Power Spectrum ◽  
Discrete Wavelet Transform ◽  
Wave Phenomenon ◽  
Discrete Wavelet ◽  
Almost All

We use the Discrete Wavelet Transform in order to study the power spectrum of data obtained in magnetoencephalographical measurements. α-wave phenomenon is found to occur independently 1/fβ noise, which is present over almost all channels. the β value is close to 1.

scholarly journals Long-term trend analysis of rainfall using hybrid Discrete Wavelet Transform (DWT) based Mann-Kendall tests in central Gujarat region, India

MAUSAM ◽  
2021 ◽  
Vol 71 (2) ◽  
pp. 209-224
Author(s):  
RAJANI NIRAV V ◽  
TIWARI MUKESH K ◽  
CHINCHORKAR S S
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Trend Analysis ◽  
Time Scales ◽  
Series Data ◽  
Trend Test ◽  
Discrete Wavelet ◽  
Rainfall Time Series

Trend analysis has become one of the most important issues in hydro-meteorological variables study due to climate change and the focus given to it in the recent past from the scientific community. In this study, long-term trends of rainfall are analyzed in eight stations located in semi-arid central Gujarat region, India by considering time series data of 116 years (1901-2016). Discrete wavelet transform (DWT) as a dyadic arrangement of continuous wavelet transformation combined with the widely applied and acknowledged Mann-Kendall (MK) trend analysis method were applied for analysis of trend and dominant periodicities in rainfall time series at monthly, annual and monsoonal time scales. Initially, rainfall time series applied in this study were decomposed using DWT to generate sub-time series at high and low frequencies, before applying the MK trend test. Further, the Sequential Mann-Kendall (SQMK) test was also applied to find out the trend changing points. The result showed that at the monthly annual and monsoon time scales, the trends in rainfall were significantly decreasing in most of the station. The 4-month and 8-month components were found as dominant at the monthly time series and the 2-year and 4-year component were found as dominant at the monsoon time series, whereas the 2-year components were observed as dominant in the annual time scale.

scholarly journals Two-Dimensional Continuous Wavelet Analysis and Its Application to Meteorological Data

2010 ◽  
Vol 27 (4) ◽  
pp. 652-666 ◽  
Author(s):  
Ning Wang ◽  
Chungu Lu
Keyword(s):  
Spectral Analysis ◽  
Wavelet Transform ◽  
Meteorological Data ◽  
Spectral Information ◽  
Discrete Wavelet ◽  
Continuous Wavelet ◽  
Two Dimensional ◽  
Test Functions ◽  
Practical Application ◽  
Continuous Analysis

Abstract The two-dimensional continuous wavelet transform (2D CWT) has become an important tool to examine and diagnose nonstationary datasets on the plane. Compared with traditional spectral analysis methods, the 2D CWT provides localized spectral information of the analyzed dataset. It also has the advantage over the 2D discrete wavelet transform (DWT) in that it covers the domain of the analyzed data with a continuous analysis from which detailed, shift-invariant spectral information of different positions and orientations can be obtained. In this paper, a brief introduction of the 2D CWT and some of the most common wavelet mother functions are given, and some practical issues arising from the implementation and applications of the 2D CWT are discussed. The 2D CWT is applied to several test functions to illustrate the effects of the transforms. To demonstrate its practical application, the 2D CWT is used to analyze a set of meteorological data obtained from a numerical model stimulation.

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