Filtering out high frequencies in time series using F-transform with respect to raised cosine generalized uniform fuzzy partition

2015 ◽  
Author(s):  
Linh Nguyen ◽  
Vilem Novak
Keyword(s):  
Time Series ◽  
High Frequencies ◽  
Fuzzy Partition

scholarly journals Testing for Linear and Nonlinear Gaussian Processes in Nonstationary Time Series

2015 ◽  
Vol 25 (01) ◽  
pp. 1550013 ◽  
Author(s):  
Ricardo Araújo Rios ◽  
Michael Small ◽  
Rodrigo Fernandes de Mello
Keyword(s):  
Time Series ◽  
Gaussian Processes ◽  
Synthetic Data ◽  
Surrogate Data ◽  
Stationary Time Series ◽  
Decomposition Techniques ◽  
High Frequencies ◽  
New Methods ◽  
Linear And Nonlinear ◽  
The Fourier Transform

Surrogate data methods have been widely applied to produce synthetic data, while maintaining the same statistical properties as the original. By using such methods, one can analyze certain properties of time series. In this context, Theiler's surrogate data methods are the most commonly considered approaches. These are based on the Fourier transform, limiting them to be applied only on stationary time series. Consequently, time series including nonstationary behavior, such as trend, produces spurious high frequencies with Theiler's methods, resulting in inconsistent surrogates. To solve this problem, we present two new methods that combine time series decomposition techniques and surrogate data methods. These new methods initially decompose time series into a set of monocomponents and the trend. Afterwards, traditional surrogate methods are applied on those individual monocomponents and a set of surrogates is obtained. Finally, all individual surrogates plus the trend signal are combined in order to create a single surrogate series. Using this method, one can investigate linear and nonlinear Gaussian processes in time series, irrespective of the presence of nonstationary behavior.

scholarly journals Stratigraphic Noise in Time Series Derived from Ice Cores

1985 ◽  
Vol 7 ◽  
pp. 76-83 ◽  
Author(s):  
David A. Fisher ◽  
Niels Reeh ◽  
H.B. Clausen
Keyword(s):  
Time Series ◽  
Spectral Power ◽  
Ice Cores ◽  
Noise Element ◽  
High Frequencies ◽  
Important Input ◽  
Time Averages ◽  
Arctic Ice ◽  
Short Time ◽  
Snow Drifting

Because of snow drifting, two time series of any variable derived from two adjacent ice cores will differ considerably. The size and statistical nature of this noise element is discussed for two kinds of measured substance. A theory is developed and compared to data from Greenland and Canadian Arctic ice cores. In case 1, the measured substance can diffuse and the seasonal cycle degrade with time and depth, e.g. δ(18O). In case 2, the measured substance cannot diffuse, e.g. microparticles. The case 2 time series contain drift noise proportional to that in the accumulation series. For accumulation series, the spectral power is concentrated at the high frequencies, i.e. is “blue”. Such noise can be easily reduced by taking relatively short time averages. The noise in the case 1 time series, however, starts out “blue” but quickly diffuses to have a “red” character with significant power at longer wavelengths, and many decades of such series must be averaged to reduce the noise level. Because the seasonal amplitude of any given variable is an important input to the drift noise and because the seasonal amplitudes of some variable types are latitude-dependent, some sites have inherently less drift noise than others.

scholarly journals Detecting time-specific differences between temporal nonlinear curves: Analyzing data from the visual world paradigm

2015 ◽  
Vol 26 (6) ◽  
pp. 2708-2725 ◽  
Author(s):  
Jacob J Oleson ◽  
Joseph E Cavanaugh ◽  
Bob McMurray ◽  
Grant Brown
Keyword(s):  
Time Series ◽  
Temporal Evolution ◽  
Response Curves ◽  
Visual World ◽  
Visual World Paradigm ◽  
High Frequencies ◽  
Fields Of Study ◽  
Alpha Level ◽  
Within Subjects ◽  
Time Specific

In multiple fields of study, time series measured at high frequencies are used to estimate population curves that describe the temporal evolution of some characteristic of interest. These curves are typically nonlinear, and the deviations of each series from the corresponding curve are highly autocorrelated. In this scenario, we propose a procedure to compare the response curves for different groups at specific points in time. The method involves fitting the curves, performing potentially hundreds of serially correlated tests, and appropriately adjusting the overall alpha level of the tests. Our motivating application comes from psycholinguistics and the visual world paradigm. We describe how the proposed technique can be adapted to compare fixation curves within subjects as well as between groups. Our results lead to conclusions beyond the scope of previous analyses.

scholarly journals Comparative Analysis of Statistical and Analytical Techniques for the Study of GNSS Geodetic Time Series

2021 ◽  
Vol 5 (1) ◽  
pp. 21
Author(s):  
Paola Barba ◽  
Belén Rosado ◽  
Javier Ramírez-Zelaya ◽  
Manuel Berrocoso
Keyword(s):  
Time Series ◽  
Comparative Analysis ◽  
Analytical Techniques ◽  
Active Regions ◽  
Noise Levels ◽  
High Frequencies ◽  
Time Series Study ◽  
Series Study ◽  
Gnss Time Series ◽  
Geodynamic Activity

GNSS systems allow precise resolution of the geodetic positioning problem through advanced techniques of GNSS observation processing (PPP or relative positioning). Current instrumentation and communications capabilities allow obtaining geocentric and topocentric geodetic high frequencies time series, whose analysis provides knowledge of the tectonic or volcanic geodynamic activity of a region. In this work, the GNSS time series study was carried out through the use and adaptation of R packets to determine their behavior, obtaining displacement velocities, noise levels, precursors in the time series, anomalous episodes, and their temporal forecast. Statistical and analytical methods were studied, for example, ARMA, ARIMA models, least-squares methods, wavelet functions, and Kalman techniques. To carry out a comparative analysis of these techniques and methods, significant GNSS time series obtained in geodynamically active regions (tectonic and/or volcanic) were considered.

scholarly journals Filtering out high frequencies in time series using F-transform

2014 ◽  
Vol 274 ◽  
pp. 192-209 ◽  
Author(s):  
Vilém Novák ◽  
Irina Perfilieva ◽  
Michal Holčapek ◽  
Vladik Kreinovich
Keyword(s):  
Time Series ◽  
High Frequencies

scholarly journals Frequency-specific suppression of locomotor components by the white+ gene in Drosophila melanogaster adult flies

2020 ◽  
Author(s):  
Chengfeng Xiao ◽  
Shuang Qiu
Keyword(s):  
Time Series ◽  
Drosophila Melanogaster ◽  
High Frequency ◽  
Copy Number ◽  
Path Length ◽  
High Frequencies ◽  
Eye Color ◽  
Specific Suppression ◽  
Over Time ◽  
Color Gene

AbstractThe classic eye-color gene white+ (w+) in Drosophila melanogaster (fruitfly) has unexpected behavioral consequences. How w+ affect locomotion of adult flies is largely unknown. Here, we show that w+ selectively suppresses locomotor components at relatively high frequencies (> 0.1 Hz). The wildtype Canton-S male flies walked intermittently in circular arenas while the white-eyed w1118 flies walked continuously. Through careful control of genetic and cytoplasmic backgrounds, we found that w+ was associated with intermittent walking. w+-carrying male flies had smaller median values of path length per second (PPS) and reduced 5-min path length compared with w1118-carrying males. Additionally, flies carrying 2-4 genomic copies of mini-white+ (mw+) showed reduced median PPSs and decreased 5-min path length compared with w1118 flies, and the suppression was dependent on the copy number of mw+. Fourier transform of the time series (i.e. PPSs over time) indicated that w+/mw+ specifically suppressed the locomotor components at relatively high frequencies (> 0.1 Hz). Lastly, the downregulation of w+ in neurons but not glial cells resulted in an increased percentage of high-frequency locomotor components. We concluded that w+ suppressed the locomotion of adult flies by selectively reducing the high-frequency locomotor components.

scholarly journals Stratigraphic Noise in Time Series Derived from Ice Cores

1985 ◽  
Vol 7 ◽  
pp. 76-83 ◽  
Author(s):  
David A. Fisher ◽  
Niels Reeh ◽  
H.B. Clausen
Keyword(s):  
Time Series ◽  
Spectral Power ◽  
Ice Cores ◽  
Noise Element ◽  
High Frequencies ◽  
Important Input ◽  
Time Averages ◽  
Arctic Ice ◽  
Short Time ◽  
Snow Drifting

Because of snow drifting, two time series of any variable derived from two adjacent ice cores will differ considerably. The size and statistical nature of this noise element is discussed for two kinds of measured substance. A theory is developed and compared to data from Greenland and Canadian Arctic ice cores. In case 1, the measured substance can diffuse and the seasonal cycle degrade with time and depth, e.g. δ(18O). In case 2, the measured substance cannot diffuse, e.g. microparticles. The case 2 time series contain drift noise proportional to that in the accumulation series. For accumulation series, the spectral power is concentrated at the high frequencies, i.e. is “blue”. Such noise can be easily reduced by taking relatively short time averages. The noise in the case 1 time series, however, starts out “blue” but quickly diffuses to have a “red” character with significant power at longer wavelengths, and many decades of such series must be averaged to reduce the noise level. Because the seasonal amplitude of any given variable is an important input to the drift noise and because the seasonal amplitudes of some variable types are latitude-dependent, some sites have inherently less drift noise than others.

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