Introduction to ECG Time Series Variability Analysis: A Simple Overview

2017 ◽  
pp. 1-12
Author(s):  
Herbert F. Jelinek ◽  
David J. Cornforth ◽  
Ahsan H. Khandoker
Keyword(s):  
Time Series ◽  
Variability Analysis

scholarly journals The Empirical Adaptive Wavelet Decomposition (EAWD): An adaptive decomposition for the variability analysis of observation time series in atmospheric science

2022 ◽  
Author(s):  
Olivier Delage ◽  
Thierry Portafaix ◽  
Hassan Bencherif ◽  
Alain Bourdier ◽  
Emma Lagracie
Keyword(s):  
Time Series ◽  
Time Scales ◽  
Observation Time ◽  
Stationary Time Series ◽  
Original Time Series ◽  
Adaptive Wavelet ◽  
Time Frequency ◽  
Variability Analysis ◽  
Original Time ◽  
Different Time Scales

Abstract. Most observational data sequences in geophysics can be interpreted as resulting from the interaction of several physical processes at several time and space scales. As a consequence, measurements time series have often characteristics of non-linearity and non-stationarity and thereby exhibit strong fluctuations at different time-scales. The variability analysis of a time series consists in decomposing it into several mode of variability, each mode representing the fluctuations of the original time series at a specific time-scale. Such a decomposition enables to obtain a time-frequency representation of the original time series and turns out to be very useful to estimate the dimensionality of the underlying dynamics. Decomposition techniques very well suited to non-linear and non-stationary time series have recently been developed in the literature. Among the most widely used of these technics are the empirical mode decomposition (EMD) and the empirical wavelet transformation (EWT). The purpose of this paper is to present a new adaptive filtering method that combines the advantages of the EMD and EWT technics, while remaining close to the dynamics of the original signal made of atmospheric observations, which means reconstructing as close as possible to the original time series, while preserving its variability at different time scales.

scholarly journals Time Series Trend and Variability Analysis of Temperature and Rainfall in West Shewa Zone of Oromia, Ethiopia

2021 ◽  
Author(s):  
Daniel Assefa ◽  
Mesfin Mengistu
Keyword(s):  
Time Series ◽  
Minimum Temperature ◽  
Kendall Test ◽  
Annual Rainfall ◽  
Maximum Temperature ◽  
Variability Analysis ◽  
Increasing Trend ◽  
Systematic Planning ◽  
Temperature Records ◽  
C 30

Abstract BackgroundThe paper focus on time series trend and variability analysis of observed rainfall and temperature records from 16 stations during 1985-2015. ResultsBoth the summer and annual rainfall have an increasing trend but not statistically significant. Regards to variability, low to very high levels of variability were recorded according to the seasons and annual rainfall, whereas, moderate to extremely high levels of variability were observed. The result of the Mann Kendall test portrays that the mean minimum temperature was raised by 0.05 oC, while the maximum temperature was increased rose by 0.03 oC/30 years. The monthly maximum temperature also shows an increasing trend with the lowest record during August (22.05 oC) and the highest in the March (26.49 oC) except in the month of November and December. Similarly, an increasing trend was observed with a mean monthly minimum temperature with the lowest mean of 8.42Co in December and the highest mean of 11.12 oC recorded in April. Besides, a low level of variability was seen both in the case of minimum and maximum temperature were observed in all months. ConclusionsTherefore, since the observed trends of both temperature and total rainfall show abnormal shifts, there is an urgent need for policymakers to design systematic planning and management activities to rain-fed agriculture.

scholarly journals Fast time series detrending with applications to heart rate variability analysis

2019 ◽  
Vol 30 (09) ◽  
pp. 1950069
Author(s):  
M. Andrecut
Keyword(s):  
Heart Rate ◽  
Time Series ◽  
Heart Rate Variability ◽  
Diffusion Equation ◽  
Time Series Analysis ◽  
Heart Rate Variability Analysis ◽  
Fast Time ◽  
Variability Analysis ◽  
Long Time ◽  
Hrv Analysis

In this paper, we discuss a new fast detrending method for the nonstationary RR time series used in Heart Rate Variability (HRV) analysis. The described method is based on the diffusion equation, and we show numerically that it is equivalent to the widely used Smoothing Priors Approach (SPA) and Wavelet Smoothing Approach (WSA) methods. The speed of the proposed method is comparable to the WSA method and it is several orders of magnitude faster than the SPA method, which makes it suitable for very long time series analysis.

scholarly journals Homogeneity of Gridded Precipitation Datasets for the Colorado River Basin

2010 ◽  
Vol 49 (12) ◽  
pp. 2404-2415 ◽  
Author(s):  
Galina Guentchev ◽  
Joseph J. Barsugli ◽  
Jon Eischeid
Keyword(s):  
Time Series ◽  
River Basin ◽  
Colorado River ◽  
Meteorological Data ◽  
Grid Cell ◽  
Colorado River Basin ◽  
Variability Analysis ◽  
Upper Colorado River Basin ◽  
The Pacific ◽  
Rapid Shift

Abstract Inhomogeneity in gridded meteorological data may arise from the inclusion of inhomogeneous station data or from aspects of the gridding procedure itself. However, the homogeneity of gridded datasets is rarely questioned, even though an analysis of trends or variability that uses inhomogeneous data could be misleading or even erroneous. Three gridded precipitation datasets that have been used in studies of the Upper Colorado River basin were tested for homogeneity in this study: that of Maurer et al., that of Beyene and Lettenmaier, and the Parameter–Elevation Regressions on Independent Slopes Model (PRISM) dataset of Daly et al. Four absolute homogeneity tests were applied to annual precipitation amounts on a grid cell and on a hydrologic subregion spatial scale for the periods 1950–99 and 1916–2006. The analysis detects breakpoints in 1977 and 1978 at many locations in all three datasets that may be due to an anomalously rapid shift in the Pacific decadal oscillation. One dataset showed breakpoints in the 1940s that might be due to the widespread change in the number of available observing stations used as input for that dataset. The results also indicated that the time series from the three datasets are sufficiently homogeneous for variability analysis during the 1950–99 period when aggregated on a subregional scale.

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