Spectral analysis of climatological time series: On the performance of periodogram, non-integer and maximum entropy methods

1988 ◽  
Vol 39 (1) ◽  
pp. 40-53 ◽  
Author(s):  
E. O. Oladipo
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Maximum Entropy ◽  
Entropy Methods

Separation of Stochastic and Deterministic Information from Seismological Time Series with Nonlinear Dynamics and Maximum Entropy Methods

2007 ◽  
Author(s):  
Rafael M. Gutiérrez ◽  
Gina M. Useche ◽  
Elias Buitrago ◽  
Kevin H. Knuth ◽  
Ariel Caticha ◽  
...  
Keyword(s):  
Nonlinear Dynamics ◽  
Time Series ◽  
Maximum Entropy ◽  
Entropy Methods

Choice of operator length for maximum entropy spectral analysis

Geophysics ◽  
1978 ◽  
Vol 43 (7) ◽  
pp. 1384-1391 ◽  
Author(s):  
James G. Berryman
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Maximum Entropy ◽  
Empirical Evidence ◽  
Discrete Time ◽  
Seismic Data ◽  
A Priori ◽  
Evidence Based ◽  
Maximum Entropy Spectral Analysis ◽  
Discrete Time Series

Empirical evidence based on maximum entropy spectra of real seismic data suggests that M = 2N/ln 2N is a reasonable a priori choice of the operator length M for discrete time series of length N. Various examples support this conclusion.

scholarly journals Periodicities in the time-series of the annual mean temperatures of Central England

MAUSAM ◽  
2021 ◽  
Vol 48 (1) ◽  
pp. 41-44
Author(s):  
R.P. KANE ◽  
N.B. TRIVEDI
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Maximum Entropy ◽  
Sunspot Number ◽  
Temperature Series ◽  
Number Series ◽  
Maximum Entropy Spectral Analysis ◽  
Mean Temperature ◽  
Good Comparison

ABSTRACT .Maximum Entropy spectral Analysis (MESA) of the 8IUlua1 mean temperature series for Central England for 1659-1991 indicated significant periodicilies at T = 7.8, 11.1, 12.5, 15, 18, 23, 32, 37, 68, 81, l09 and 203 years. These compare well with T = 22, 30, 80, 200 years obtained for China. Also, a good comparison is obtained with some periodicities in the sunspot number series.    

scholarly journals Improving predictability of time series using maximum entropy methods

2015 ◽  
Vol 110 (1) ◽  
pp. 10003 ◽  
Author(s):  
G. Chliamovitch ◽  
A. Dupuis ◽  
A. Golub ◽  
B. Chopard
Keyword(s):  
Time Series ◽  
Maximum Entropy ◽  
Entropy Methods

Periodicities in Atlantic Coast Striped Bass (Morone saxatilis) Commercial Fisheries Data

1979 ◽  
Vol 36 (1) ◽  
pp. 54-62 ◽  
Author(s):  
Webster Van Winkle ◽  
Bernadette L. Kirk ◽  
Bert W. Rust
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Maximum Entropy ◽  
Striped Bass ◽  
Atlantic Coast ◽  
Morone Saxatilis ◽  
Commercial Fisheries ◽  
Time Interval ◽  
Maximum Entropy Spectral Analysis ◽  
The North

Atlantic Coast striped bass (Morone saxatilis) commercial fisheries data are examined for periodicities in the appearance of dominant year-classes using autocorrelation and spectral-analysis techniques. Results obtained using maximum entropy and classical Fourier spectral-analysis methods are compared. Statistically significant periodicities of approximately 20 yr and of 6–8 yr are common to the time series for most states and regions. Since the periodicities are neither very pronounced nor simple, it is difficult to isolate the causative factors, which are more likely to be density-independent environmental factors enhancing survival of the young than intrinsic characteristics of the life cycle of striped bass. The spectra for Maryland landings and Maryland landings per unit gear are nearly identical, suggesting that at least for Maryland the landings and landings-per-unit-gear data are approximately equally reliable as indices of stock size. The structure of the North Carolina time series is unique, which supports the opinion that this stock does not mix appreciably with Chesapeake and Hudson stocks. Impact assessments and monitoring programs should not be predicted on the expectation of pronounced or simple periodicities of 6 years or any other time interval in the appearance of dominant year-classes in Atlantic Coast striped bass populations. Key words: autocorrelation analysis, commercial fisheries data, Fourier spectral analysis, maximum entropy spectral analysis, periodicities, striped bass

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