scholarly journals Solar-Like Oscillations of Procyon A: Stellar Models and Time Series Simulations versus Observations

2000 ◽  
Vol 176 ◽  
pp. 461-462
Author(s):  
C. Barban ◽  
E. Michel ◽  
M. Martic ◽  
J. Schmitt ◽  
J. C. Lebrun ◽  
...  
Keyword(s):  
Time Series ◽  
Power Spectrum ◽  
Numerical Simulations ◽  
Observational Data ◽  
Theoretical Predictions ◽  
Stellar Models ◽  
Excess Power ◽  
New Evidence

AbstractThe aim of this paper (further developed in Barban et al. 1999) is to present new evidence of the possible stellar origin of the observed excess power in the power spectrum of Procyon A presented in Martic et al. (1999) by comparing these observational data with theoretical predictions and numerical simulations.

scholarly journals Search for p-mode Frequencies on Procyon A

2001 ◽  
Vol 203 ◽  
pp. 121-124 ◽  
Author(s):  
M. Martic ◽  
J. C. Lebrun ◽  
J. Schmitt ◽  
J.-L. Bertaux
Keyword(s):  
Time Series ◽  
Frequency Spectrum ◽  
Doppler Shift ◽  
Power Spectra ◽  
Stellar Models ◽  
The Individual ◽  
Excess Power

Following the recent evidence for the presence of an excess of power around 1 mHz in the frequency spectrum of the Doppler shift measurements for Procyon (Martic et al., 1999), we searched for individual frequencies of p-modes from three independent observing runs (5, 10 and 15 nights). All observations (December 1997, November 1998, January 1999) were made with the ELODIE spectrograph on the 1.93 m telescope at Observatoire de Haute Provence. The individual peaks in cleaned power spectra of each time series in the interval of excess power are compared with the predicted p-mode frequencies from stellar models (Chaboyer et al., 1999) for Procyon A.

Fractal Analysis of Event Related Potential

1993 ◽  
Vol 5 (2) ◽  
pp. 198-201
Author(s):  
Hideto Ide ◽  
◽  
Shinjiro Yagi
Keyword(s):  
Time Series ◽  
Fractal Dimension ◽  
Power Spectrum ◽  
Observational Data ◽  
Fractal Analysis ◽  
Event Related Potential

We have tried to apply fractal analysis to time series which have 1/f power spectrum. Before carrying out any analysis, we expand the idea of fractal to time series. We examine the fractal dimension of time series to simulate the Brawnian function. We apply fractal analysis to observational data of event related potential (ERP) and compare averaging results with those based on fractal analysis.

Transmission of Elegant Laguerre-Gaussian beams at a dielectric interface - numerical simulations

2009 ◽  
Vol 57 (2) ◽  
pp. 181-188 ◽  
Author(s):  
W. Szabelak ◽  
W. Nasalski
Keyword(s):  
Numerical Simulations ◽  
Total Internal Reflection ◽  
Polarization Effect ◽  
Internal Reflection ◽  
Gaussian Beams ◽  
Cross Polarization ◽  
Dielectric Interface ◽  
Theoretical Predictions ◽  
Critical Incidence

Transmission of Elegant Laguerre-Gaussian beams at a dielectric interface - numerical simulations Behaviour of Laguerre-Gaussian beams impinged at a dielectric interface under distinct angles is discussed. For different incident angles the beams interact with the interface differently. Two ranges of incident angles, specified by a position of a spectral cone of beam field and related to a cross-polarization effect, are analyzed. Boundary between these two ranges is defined. Cases of critical incidence and total internal reflection are also discussed. Paraxial beams near the lower paraxial limit are considered. Theoretical predictions are confirmed by numerical simulations.

scholarly journals Analysis of Dynamical Behavior for Epidemic Disease COVID-19 with Application

2021 ◽  
Vol 12 (4) ◽  
pp. 568-577
Author(s):  
Dr. Maysoon M. Aziz, Et. al.
Keyword(s):  
Time Series ◽  
Linear System ◽  
Power Spectrum ◽  
Dynamical Behavior ◽  
Simulated Data ◽  
Real Data ◽  
Sir Model ◽  
Epidemic Disease ◽  
The World ◽  
Non Linear System

In this paper, we will use the differential equations of the SIR model as a non-linear system, by using the Runge-Kutta numerical method to calculate simulated values for known epidemiological diseases related to the time series including the epidemic disease COVID-19, to obtain hypothetical results and compare them with the dailyreal statisticals of the disease for counties of the world and to know the behavior of this disease through mathematical applications, in terms of stability as well as chaos in many applied methods. The simulated data was obtained by using Matlab programms, and compared between real data and simulated datd were well compatible and with a degree of closeness. we took the data for Italy as an application.  The results shows that this disease is unstable, dissipative and chaotic, and the Kcorr of it equal (0.9621), ,also the power spectrum system was used as an indicator to clarify the chaos of the disease, these proves that it is a spread,outbreaks,chaotic and epidemic disease .

scholarly journals Time series analysis of trial-to-trial variability of MEG power spectrum during rest state, unattented listening and frequency-modulated tones classification

2021 ◽  
Author(s):  
Lech Kipiński ◽  
Wojciech Kordecki
Keyword(s):  
Stochastic Process ◽  
Time Series ◽  
Power Spectrum ◽  
Spectral Density ◽  
Cognitive Task ◽  
Clinical Neuroscience ◽  
Rest State ◽  
Short Time ◽  
Low Complex ◽  
Dominant Frequencies

AbstractThe nonstationarity of EEG/MEG signals is important for understanding the functioning of human brain. From the previous research we know that even very short, i.e. 250—500ms MEG signals are variance-nonstationary. The covariance of stochastic process is mathematically associated with its spectral density, therefore we investigate how the spectrum of such nonstationary signals varies in time.We analyze the data from 148-channel MEG, that represent rest state, unattented listening and frequency-modulated tones classification. We transform short-time MEG signals to the frequency domain using the FFT algorithm and for the dominant frequencies 8—12 Hz we prepare the time series representing their trial-to-trial variability. Then, we test them for level- and trend-stationarity, unit root, heteroscedasticity and gaussianity and based on their properties we propose the ARMA-modelling for their description.The analyzed time series have the weakly stationary properties independently of the functional state of brain and localization. Only their small percentage, mostly related to the cognitive task, still presents nonstationarity. The obtained mathematical models show that the spectral density of analyzed signals depends on only 2—3 previous trials.The presented method has limitations related to FFT resolution and univariate models, but it is not computationally complicated and allows to obtain a low-complex stochastic models of the EEG/MEG spectrum variability.Although the physiological short-time MEG signals are in principle nonstationary in time domain, its power spectrum at the dominant frequencies varies as weakly stationary stochastic process. Described technique has the possible applications in prediction of the EEG/MEG spectral properties in theoretical and clinical neuroscience.

scholarly journals Forecasting stock price movement: new evidence from a novel hybrid deep learning model

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yang Zhao ◽  
Zhonglu Chen
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Hybrid Model ◽  
Stock Price ◽  
Linear Time ◽  
Price Movement ◽  
Content Type ◽  
Non Linear ◽  
New Evidence ◽  
Deep Learning Model

PurposeThis study explores whether a new machine learning method can more accurately predict the movement of stock prices.Design/methodology/approachThis study presents a novel hybrid deep learning model, Residual-CNN-Seq2Seq (RCSNet), to predict the trend of stock price movement. RCSNet integrates the autoregressive integrated moving average (ARIMA) model, convolutional neural network (CNN) and the sequence-to-sequence (Seq2Seq) long–short-term memory (LSTM) model.FindingsThe hybrid model is able to forecast both linear and non-linear time-series component of stock dataset. CNN and Seq2Seq LSTMs can be effectively combined for dynamic modeling of short- and long-term-dependent patterns in non-linear time series forecast. Experimental results show that the proposed model outperforms baseline models on S&P 500 index stock dataset from January 2000 to August 2016.Originality/valueThis study develops the RCSNet hybrid model to tackle the challenge by combining both linear and non-linear models. New evidence has been obtained in predicting the movement of stock market prices.

scholarly journals Wavelet analysis applied on temporal data sets in order to reveal possible pre-seismic radio anomalies and comparison with the trend of the raw data 

2021 ◽  
Author(s):  
Giovanni Nico ◽  
Pier Francesco Biagi ◽  
Anita Ermini ◽  
Mohammed Yahia Boudjada ◽  
Hans Ulrich Eichelberger ◽  
...  
Keyword(s):  
Time Series ◽  
Wavelet Analysis ◽  
Power Spectrum ◽  
Power Spectra ◽  
Wavelet Spectrum ◽  
Time Signal ◽  
Time Behavior ◽  
Sudden Increase ◽  
Night Time ◽  
Raw Data

<p>Since 2009, several radio receivers have been installed throughout Europe in order to realize the INFREP European radio network for studying the VLF (10-50 kHz) and LF (150-300 kHz) radio precursors of earthquakes. Precursors can be related to “anomalies” in the night-time behavior of  VLF signals. A suitable method of analysis is the use of the Wavelet spectra.  Using the “Morlet function”, the Wavelet transform of a time signal is a complex series that can be usefully represented by its square amplitude, i.e. considering the so-called Wavelet power spectrum.</p><p>The power spectrum is a 2D diagram that, once properly normalized with respect to the power of the white noise, gives information on the strength and precise time of occurrence of the various Fourier components, which are present in the original time series. The main difference between the Wavelet power spectra and the Fourier power spectra for the time series is that the former identifies the frequency content along the operational time, which cannot be done with the latter. Anomalies are identified as regions of the Wavelet spectrogram characterized by a sudden increase in the power strength.</p><p>On January 30, 2020 an earthquake with Mw= 6.0 occurred in Dodecanese Islands. The results of the Wavelet analysis carried out on data collected some INFREP receivers is compared with the trends of the raw data. The time series from January 24, 2020 till January 31, 2000 was analyzed. The Wavelet spectrogram shows a peak corresponding to a period of 1 day on the days before January 30. This anomaly was found for signals transmitted at the frequencies 19,58 kHz, 20, 27 kHz, 23,40 kHz with an energy in the peak increasing from 19,58 kHz to 23,40 kHz. In particular, the signal at the frequency 19,58 kHz, shows a peak on January 29, while the frequencies 20,27 kHz and 23,40 kHz are characterized by a peak starting on January 28 and continuing to January 29. The results presented in this work shows the perspective use of the Wavelet spectrum analysis as an operational tool for the detection of anomalies in VLF and LF signal potentially related to EQ precursors.</p>

LONG RANGE CORRELATION PROPERTIES OF AFTERSHOCK RELAXATION SIGNALS

Fractals ◽  
1995 ◽  
Vol 03 (04) ◽  
pp. 839-847 ◽  
Author(s):  
A. VESPIGNANI ◽  
A. PETRI ◽  
A. ALIPPI ◽  
G. PAPARO ◽  
M. COSTANTINI
Keyword(s):  
Time Series ◽  
Acoustic Emission ◽  
Statistical Analysis ◽  
Power Spectrum ◽  
Power Law ◽  
Amplitude Distribution ◽  
Relaxation Processes ◽  
Critical Dynamics ◽  
Range Correlation ◽  
Correlation Properties

Relaxation processes taking place after microfracturing of laboratory samples give rise to ultrasonic acoustic emission signals. Statistical analysis of the resulting time series has revealed many features which are characteristic of critical phenomena. In particular, the autocorrelation functions obey a power-law behavior, implying a power spectrum of the kind 1/f. Also the amplitude distribution N(V) of such signals follows a power law, and the obtained exponents are consistent with those found in other experiments: N(V) dV≃V–γ dV, with γ=1.7±0.2. We also analyzed the distribution N(τ) of the delay time τ between two consecutive acoustic emission events. We found that a N(τ) distribution rather close to a power law constitutes a common feature of all the recorded signals. These experimental results can be considered as a striking evidence for a critical dynamics underlying the microfracturing processes.

scholarly journals The Two-Dimensional Power Spectrum of the Las Campanas Redshift Survey: Detection of Excess Power on 100 [ITAL]h[/ITAL][TSUP]−1[/TSUP] Mpc Scales

1996 ◽  
Vol 456 (1) ◽  
Author(s):  
Stephen D. Landy ◽  
Stephen A. Shectman ◽  
Huan Lin ◽  
Robert P. Kirshner ◽  
Augustus A. Oemler ◽  
...  
Keyword(s):  
Power Spectrum ◽  
Two Dimensional ◽  
Redshift Survey ◽  
Excess Power

Bohm's Mysterious 'Quantum Force' and 'Active Information': Alternative Interpretation and Statistical Properties

2001 ◽  
Vol 56 (1-2) ◽  
pp. 205-207 ◽  
Author(s):  
Boon Leong Lan
Keyword(s):  
Time Series ◽  
Power Spectrum ◽  
Important Implication ◽  
Alternative Interpretation ◽  
Quantum States ◽  
Statistical Properties ◽  
Numerical Evidence ◽  
Stationary Quantum ◽  
Non Gaussian ◽  
Quantum Force

AbstractAn alternative interpretation to Bohm’s ‘quantum force’ and ‘active information’ is proposed. Numerical evidence is presented, which suggests that the time series of Bohm’s ‘quantum force’ evaluated at the Bohmian position for non-stationary quantum states are typically non-Gaussian stable distributed with a flat power spectrum in classically chaotic Hamitonian systems. An important implication of these statistical properties is briefly mentioned

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