Geophysical Time Series and Climatic Change

2002 ◽  
pp. 109-128
Author(s):  
V. Klemeš
Keyword(s):  
Time Series ◽  
Climatic Change ◽  
Geophysical Time Series

Wavelet Entropy Energy Measure (WEEM): A multiscale measure to grade a geophysical system's predictability

2021 ◽  
Author(s):  
Ravi Kumar Guntu ◽  
Ankit Agarwal
Keyword(s):  
Time Series ◽  
Energy Distribution ◽  
Energy Concentration ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Wavelet Entropy ◽  
Wavelet Energy ◽  
Energy Measure ◽  
Geophysical Time Series ◽  
Geophysical System

<p>Model-free gradation of predictability of a geophysical system is essential to quantify how much inherent information is contained within the system and evaluate different forecasting methods' performance to get the best possible prediction. We conjecture that Multiscale Information enclosed in a given geophysical time series is the only input source for any forecast model. In the literature, established entropic measures dealing with grading the predictability of a time series at multiple time scales are limited. Therefore, we need an additional measure to quantify the information at multiple time scales, thereby grading the predictability level. This study introduces a novel measure, Wavelet Entropy Energy Measure (WEEM), based on Wavelet entropy to investigate a time series's energy distribution. From the WEEM analysis, predictability can be graded low to high. The difference between the entropy of a wavelet energy distribution of a time series and entropy of wavelet energy of white noise is the basis for gradation. The metric quantifies the proportion of the deterministic component of a time series in terms of energy concentration, and its range varies from zero to one. One corresponds to high predictable due to its high energy concentration and zero representing a process similar to the white noise process having scattered energy distribution. The proposed metric is normalized, handles non-stationarity, independent of the length of the data. Therefore, it can explain the evolution of predictability for any geophysical time series (ex: precipitation, streamflow, paleoclimate series) from past to the present. WEEM metric's performance can guide the forecasting models in getting the best possible prediction of a geophysical system by comparing different methods. </p>

A wavelet based method to remove the long term periodicities of geophysical time series

2020 ◽  
Vol 66 (2) ◽  
pp. 299-306
Author(s):  
M.J.A. Bolzan ◽  
A.M.S. Franco ◽  
E. Echer
Keyword(s):  
Time Series ◽  
Geophysical Time Series

scholarly journals Continuous partial trends and low-frequency oscillations of time series

2005 ◽  
Vol 12 (4) ◽  
pp. 451-460 ◽  
Author(s):  
A. R. Tomé ◽  
P. M. A. Miranda
Keyword(s):  
Time Series ◽  
Low Frequency ◽  
Distributed Data ◽  
Spatially Distributed ◽  
Geophysical Time Series ◽  
Slow Evolution ◽  
Using Data ◽  
The Times ◽  
Data Subject

Abstract. This paper presents a recent methodology developed for the analysis of the slow evolution of geophysical time series. The method is based on least-squares fitting of continuous line segments to the data, subject to flexible conditions, and is able to objectively locate the times of significant change in the series tendencies. The time distribution of these breakpoints may be an important set of parameters for the analysis of the long term evolution of some geophysical data, simplifying the intercomparison between datasets and offering a new way for the analysis of time varying spatially distributed data. Several application examples, using data that is important in the context of global warming studies, are presented and briefly discussed.

Detecting hydro-climatic change using spatiotemporal analysis of rainfall time series in Western Algeria

2012 ◽  
Vol 65 (3) ◽  
pp. 1293-1311 ◽  
Author(s):  
Leila Hamlaoui-Moulai ◽  
Mohammed Mesbah ◽  
Doudja Souag-Gamane ◽  
Abderrahmane Medjerab
Keyword(s):  
Time Series ◽  
Climatic Change ◽  
Spatiotemporal Analysis ◽  
Rainfall Time Series ◽  
Western Algeria

A SAS-IML program for implementing two-phase regression analysis of geophysical time series data

1997 ◽  
Vol 23 (7) ◽  
pp. 763-770 ◽  
Author(s):  
Gerri M. Dunnigan ◽  
John L. Hammen ◽  
T.Robert Harris
Keyword(s):  
Time Series ◽  
Regression Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Two Phase ◽  
Geophysical Time Series
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