Identification of Potential Risks to System Security Using Wavelet Analysis, the Time-and-Frequency Distribution Indicator of the Time Series and the Correlation Analysis of Wavelet-Spectra

Amosov, O.S.; Amosova, S.G.; Muller, Nina V.

doi:10.1109/fareastcon.2018.8602675

Cited by 7 publications

(4 citation statements)

References 4 publications

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“…To identify the features of the fractional structure of the process, fractality indicators such as the Hurst parameter are used. Several methods are used to estimate the Hurst parameter, including: rescaled range analysis (the R/S method), variance change graph analysis, and wavelet analysis [ 13 , 14 ]. According to the R/S method, the Hurst parameter of a time series is calculated by the following formula:

where R is the range of the first n cumulative deviations from the mean, S is the standard deviation of observations, and n is the number of observations.…”

Section: Fractal Process Modelmentioning

confidence: 99%

“…Recently, interest in the class of non-stationary processes that have a fractal nature has deepened [ 1 , 2 , 3 , 5 , 6 , 7 , 8 , 9 , 10 , 14 , 18 , 19 ].…”

Section: Mathematical Model Of Inertial Sensor Errors In Navigation Systems With Respect To the Chaotic Indicatormentioning

confidence: 99%

“…Analyses of network telecommunication traffic result in time series (TS) analyses most widely used in traditional methods of statistically analyzing random variable functions. Alongside with traditional methods the signal processing methods based on fractal and wavelet-transformations [ 5 , 7 , 9 , 10 , 14 , 18 , 19 ], have seen more widespread use in recent years. The first of these analyses is specifically concerned with the identification of fractal properties that allow time series’ to be attributed to predetermined models.…”

Section: Information and Telecommunication Systemmentioning

confidence: 99%

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Peculiarities and Applications of Stochastic Processes with Fractal Properties

Amosov

Amosova

2021

Sensors

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In this paper, the fractal properties of stochastic processes and objects in different areas were specified and investigated. These included: measuring systems and sensors, navigation and motion controls, telecommunication systems and networks, and flaw detection technologies. Additional options that occur through the use of fractality were also indicated and exemplified for each application. Regarding the problems associated with navigation information processing, the following fractal nature processes were identified: errors of inertial sensors based on the microelectromechanical systems called MEMS, in particular gyroscopic drift and accelerometer bias, and; the trajectory movement of mobile objects. With regard to navigation problems specifically, the estimation problem statement and its solution are given by way of the Bayesian approach for processing fractal processes. The modified index of self-similarity for telecommunication series was proposed, and the self-similarity of network traffic based on the R/S method and wavelet analysis was identified. In failure detection, fractality manifested as porosity, wrinkles, surface fractures, and ultrasonic echo signals measured using non-destructive sensors used for rivet compound testing.

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where R is the range of the first n cumulative deviations from the mean, S is the standard deviation of observations, and n is the number of observations.…”

Section: Fractal Process Modelmentioning

confidence: 99%

“…Recently, interest in the class of non-stationary processes that have a fractal nature has deepened [ 1 , 2 , 3 , 5 , 6 , 7 , 8 , 9 , 10 , 14 , 18 , 19 ].…”

Section: Mathematical Model Of Inertial Sensor Errors In Navigation Systems With Respect To the Chaotic Indicatormentioning

confidence: 99%

Section: Information and Telecommunication Systemmentioning

confidence: 99%

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Peculiarities and Applications of Stochastic Processes with Fractal Properties

Amosov

Amosova

2021

Sensors

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“…The analysis is based on determining a signal distribution index in its representation in the frequency-time domain. Their results analyze the correlation of the spectra obtained with frequency indicators in the signal time spaces [20].…”

Section: Introductionmentioning

confidence: 99%

Stock Market Forecasting Using Continuous Wavelet Transform and Long Short-Term Memory Neural Networks

Acuna-García¹

2022

ijarcs

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The analysis and exploitation of complex and large-volume data requires new approaches, and modeling it in time series is a very successful technique. A characteristic time series is the one that defines the dynamic financial market and its asset prices. This research presents a novel forecasting methodology, which uses the Continuous Wavelet Transform for the definition of representative elements that define a time series, and a recurrent neural network architecture for the forecast of prices of financial stocks related by the item of income in the short and medium time term. The proposed model, inspired by the Continuous Wavelet Transform and Neural Networks of the "Long short-term memory" type, uses the most representative coefficients of the Wavelet transform based on the time series in the time domain, for the prediction of future prices of stocks in short prospective periods. The results show a very successful projection using this methodology. Future research will analyze the interrelationship presented by the price time series of the same stock market section, in the domain of Wavelets, and how it affects the stock market forecast.

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Analysis of Risk Assessment Models Used to Implement the Labor Safety Management System Algorithm

Zhdakaeva

2021

Current Problems and Ways of Industry Development: Equipment and Technologies

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Identification of Potential Risks to System Security Using Wavelet Analysis, the Time-and-Frequency Distribution Indicator of the Time Series and the Correlation Analysis of Wavelet-Spectra

Cited by 7 publications

References 4 publications

Peculiarities and Applications of Stochastic Processes with Fractal Properties

Peculiarities and Applications of Stochastic Processes with Fractal Properties

Stock Market Forecasting Using Continuous Wavelet Transform and Long Short-Term Memory Neural Networks

Analysis of Risk Assessment Models Used to Implement the Labor Safety Management System Algorithm

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