scholarly journals Deriving scattering strengths from nonstationary time‐series data: A comparison of low‐frequency surface‐backscattering strengths using both impulsive and coherent sources

1995 ◽  
Vol 97 (5) ◽  
pp. 3403-3403 ◽  
Author(s):  
Roger C. Gauss ◽  
Peter M. Ogden ◽  
John B. Chester ◽  
Joseph M. Fialkowsi
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Low Frequency ◽  
Nonstationary Time Series ◽  
Series Data ◽  
Coherent Sources

scholarly journals An Approach for Reconstruction of Realistic Economic Data Based on Frequency Characteristics between IMFs

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Hao Du ◽  
Hao Gong ◽  
Suyue Han ◽  
Peng Zheng ◽  
Bin Liu ◽  
...  
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Time Series Data ◽  
Low Frequency ◽  
Test Method ◽  
Series Data ◽  
Permutation Entropy ◽  
Intrinsic Mode Functions ◽  
Economic Data ◽  
Index Method

Reconstruction of realistic economic data often causes social economists to analyze the underlying driving factors in time-series data or to study volatility. The intrinsic complexity of time-series data interests and attracts social economists. This paper proposes the bilateral permutation entropy (BPE) index method to solve the problem based on partly ensemble empirical mode decomposition (PEEMD), which was proposed as a novel data analysis method for nonlinear and nonstationary time series compared with the T-test method. First, PEEMD is extended to the case of gold price analysis in this paper for decomposition into several independent intrinsic mode functions (IMFs), from high to low frequency. Second, IMFs comprise three parts, including a high-frequency part, low-frequency part, and the whole trend based on a fine-to-coarse reconstruction by the BPE index method and the T-test method. Then, this paper conducts a correlation analysis on the basis of the reconstructed data and the related affected macroeconomic factors, including global gold production, world crude oil prices, and world inflation. Finally, the BPE index method is evidently a vitally significant technique for time-series data analysis in terms of reconstructed IMFs to obtain realistic data.

scholarly journals Estimation of Natural Selection and Allele Age from Time Series Allele Frequency Data Using a Novel Likelihood-Based Approach

Genetics ◽  
2020 ◽  
Vol 216 (2) ◽  
pp. 463-480
Author(s):  
Zhangyi He ◽  
Xiaoyang Dai ◽  
Mark Beaumont ◽  
Feng Yu
Keyword(s):  
Time Series ◽  
Natural Selection ◽  
Time Series Data ◽  
Low Frequency ◽  
Series Data ◽  
Selection Coefficient ◽  
One Dimensional ◽  
Recent Sample ◽  
Backward Equation ◽  
Recent Action

Temporally spaced genetic data allow for more accurate inference of population genetic parameters and hypothesis testing on the recent action of natural selection. In this work, we develop a novel likelihood-based method for jointly estimating selection coefficient and allele age from time series data of allele frequencies. Our approach is based on a hidden Markov model where the underlying process is a Wright-Fisher diffusion conditioned to survive until the time of the most recent sample. This formulation circumvents the assumption required in existing methods that the allele is created by mutation at a certain low frequency. We calculate the likelihood by numerically solving the resulting Kolmogorov backward equation backward in time while reweighting the solution with the emission probabilities of the observation at each sampling time point. This procedure reduces the two-dimensional numerical search for the maximum of the likelihood surface, for both the selection coefficient and the allele age, to a one-dimensional search over the selection coefficient only. We illustrate through extensive simulations that our method can produce accurate estimates of the selection coefficient and the allele age under both constant and nonconstant demographic histories. We apply our approach to reanalyze ancient DNA data associated with horse base coat colors. We find that ignoring demographic histories or grouping raw samples can significantly bias the inference results.

scholarly journals Maximum likelihood estimation of natural selection and allele age from time series data of allele frequencies

2019 ◽  
Author(s):  
Zhangyi He ◽  
Xiaoyang Dai ◽  
Mark Beaumont ◽  
Feng Yu
Keyword(s):  
Time Series ◽  
Natural Selection ◽  
Time Series Data ◽  
Low Frequency ◽  
Likelihood Estimation ◽  
Allele Frequencies ◽  
Series Data ◽  
Selection Coefficient ◽  
Backward Equation ◽  
Recent Action

AbstractTemporally spaced genetic data allow for more accurate inference of population genetic parameters and hypothesis testing on the recent action of natural selection. In this work, we develop a novel likelihood-based method for jointly estimating selection coefficient and allele age from time series data of allele frequencies. Our approach is based on a hidden Markov model where the underlying process is a Wright-Fisher diffusion conditioned to survive until the time of the most recent sample. This formulation circumvents the assumption required in existing methods that the allele is created by mutation at a certain low frequency. We calculate the likelihood by numerically solving the resulting Kolmogorov backward equation backwards in time while re-weighting the solution with the emission probabilities of the observation at each sampling time point. This procedure reduces the two-dimensional numerical search for the maximum of the likelihood surface for both the selection coefficient and the allele age to a one-dimensional search over the selection coefficient only. We illustrate through extensive simulations that our method can produce accurate estimates of the selection coefficient and the allele age under both constant and non-constant demographic histories. We apply our approach to re-analyse ancient DNA data associated with horse base coat colours. We find that ignoring demographic histories or grouping raw samples can significantly bias the inference results.

scholarly journals Extraction of instantaneous frequencies and amplitudes in nonstationary time-series data

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Daniel E. Shea ◽  
Rajiv Giridharagopal ◽  
David S. Ginger ◽  
Steven L. Brunton ◽  
J. Nathan Kutz
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Nonstationary Time Series ◽  
Series Data ◽  
Instantaneous Frequencies
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