scholarly journals On testing for the presence of hidden periodicities in time series

2015 ◽  
Vol 10 (1) ◽  
pp. 729-737
Author(s):  
Khadidja Bensouici ◽  
Zaher Mohdeb
Keyword(s):  
Time Series ◽  
Hidden Periodicities

Design Wind Speeds Using Fast Fourier Transform

2011 ◽  
pp. 1576-1591
Author(s):  
Z.. Ismail ◽  
N. H. Ramli ◽  
Z.. Ibrahim ◽  
T. A. Majid ◽  
G. Sundaraj ◽  
...  
Keyword(s):  
Time Series ◽  
Fourier Transform ◽  
Spectral Density ◽  
Fast Fourier Transform ◽  
Time Series Data ◽  
Absolute Magnitude ◽  
Stationary Pattern ◽  
Wind Speeds ◽  
Hidden Periodicities ◽  
Using Data

In this chapter, a study on the effects of transforming wind speed data, from a time series domain into a frequency domain via Fast Fourier Transform (FFT), is presented. The wind data is first transformed into a stationary pattern from a non-stationary pattern of time series data using statistical software. This set of time series is then transformed using FFT for the main purpose of the chapter. The analysis is done through MATLAB software, which provides a very useful function in FFT algorithm. Parameters of engineering significance such as hidden periodicities, frequency components, absolute magnitude and phase of the transformed data, power spectral density and cross spectral density can be obtained. Results obtained using data from case studies involving thirty-one weather stations in Malaysia show great potential for application in verifying the current criteria used for design practices.

The search for hidden periodicities in biological time series revisited

1989 ◽  
Vol 139 (4) ◽  
pp. 487-515 ◽  
Author(s):  
Harold B. Dowse ◽  
John M. Ringo
Keyword(s):  
Time Series ◽  
Biological Time ◽  
Hidden Periodicities

Design Wind Speeds Using Fast Fourier Transform

2011 ◽  
pp. 303-322
Author(s):  
Z. Ismail ◽  
N. H. Ramli ◽  
Z. Ibrahim ◽  
T. A. Majid ◽  
G. Sundaraj ◽  
...  
Keyword(s):  
Time Series ◽  
Fourier Transform ◽  
Spectral Density ◽  
Fast Fourier Transform ◽  
Time Series Data ◽  
Absolute Magnitude ◽  
Stationary Pattern ◽  
Wind Speeds ◽  
Hidden Periodicities ◽  
Using Data

In this chapter, a study on the effects of transforming wind speed data, from a time series domain into a frequency domain via Fast Fourier Transform (FFT), is presented. The wind data is first transformed into a stationary pattern from a non-stationary pattern of time series data using statistical software. This set of time series is then transformed using FFT for the main purpose of the chapter. The analysis is done through MATLAB software, which provides a very useful function in FFT algorithm. Parameters of engineering significance such as hidden periodicities, frequency components, absolute magnitude and phase of the transformed data, power spectral density and cross spectral density can be obtained. Results obtained using data from case studies involving thirty-one weather stations in Malaysia show great potential for application in verifying the current criteria used for design practices.

The wavelet detection of hidden periodicities in time series

1997 ◽  
Vol 35 (1) ◽  
pp. 9-23 ◽  
Author(s):  
Yuan Li ◽  
Zhongjie Xie
Keyword(s):  
Time Series ◽  
Hidden Periodicities

The LDE-Type Flare Occurrence (1969-1993)

1994 ◽  
Vol 144 ◽  
pp. 279-282
Author(s):  
A. Antalová
Keyword(s):  
Time Series ◽  
Fourier Analysis ◽  
Sunspot Cycle ◽  
List Type ◽  
Type Number ◽  
Solar Cycles ◽  
Type Iv ◽  
Long Duration ◽  
Cycle Maximum ◽  
Flare Index

AbstractThe occurrence of LDE-type flares in the last three cycles has been investigated. The Fourier analysis spectrum was calculated for the time series of the LDE-type flare occurrence during the 20-th, the 21-st and the rising part of the 22-nd cycle. LDE-type flares (Long Duration Events in SXR) are associated with the interplanetary protons (SEP and STIP as well), energized coronal archs and radio type IV emission. Generally, in all the cycles considered, LDE-type flares mainly originated during a 6-year interval of the respective cycle (2 years before and 4 years after the sunspot cycle maximum). The following significant periodicities were found:• in the 20-th cycle: 1.4, 2.1, 2.9, 4.0, 10.7 and 54.2 of month,• in the 21-st cycle: 1.2, 1.6, 2.8, 4.9, 7.8 and 44.5 of month,• in the 22-nd cycle, till March 1992: 1.4, 1.8, 2.4, 7.2, 8.7, 11.8 and 29.1 of month,• in all interval (1969-1992):a)the longer periodicities: 232.1, 121.1 (the dominant at 10.1 of year), 80.7, 61.9 and 25.6 of month,b)the shorter periodicities: 4.7, 5.0, 6.8, 7.9, 9.1, 15.8 and 20.4 of month.Fourier analysis of the LDE-type flare index (FI) yields significant peaks at 2.3 - 2.9 months and 4.2 - 4.9 months. These short periodicities correspond remarkably in the all three last solar cycles. The larger periodicities are different in respective cycles.

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