Kolmogorov space in time series data

Kanjamapornkul, Kabin; Pinčák, Richard

doi:10.1002/mma.3875

Cited by 20 publications

(24 citation statements)

References 31 publications

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“…We use the (ITD−IMF)chain 1 (1) transformation. The algorithm can be find in [27,53] to detect the spinor field in time series data of genetic code.The main tool is the so called (ITD − IMF)chain 1 (n) in time series prediction with time series of superspace of genetic code for real application with viral gene expression. We compare the result with traditional bioinformation string matching the representation with our new methodology of time series expression as the main result of our work.…”

Section: Methodsmentioning

confidence: 99%

“…In the modern researches of algebraic geometry [25] for time series data, there exists another approach which uses the spinor field [18,26] in the Kolmogorov space of the time series data [27] over the genetic code to represent the gene structure as the ghost [41,42] and the anti-ghost fields of the codon and the anti-codon. This is achieved in the frameworks of supersymmetry [44][45][46] and G-theory [19,22].…”

Section: Introductionmentioning

confidence: 99%

“…The specific methodology is discussed in Section V. Here we calculate the Chern-Simons current over a time series of the genetic code of V3 loop with 3 species of the HIV virus and 3 species of CD4 in the rabbit host cell. The results of the calculation are obtained by using the data analysis of (ITD − IMF)chain 1 (n) [27] and considering a time series in terms of quaternionic projective space. We find an evolution feedback between an antigene drift in V3 gene and an antigene shift in the CD4 gene from.…”

Section: Introductionmentioning

confidence: 99%

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The Chern‐Simons Current in Systems of DNA‐RNA Transcriptions

et al. 2018

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A Chern-Simons current, coming from ghost and anti-ghost fields of supersymmetry theory, can be used to define a spectrum of gene expression in new time series data where a spinor field, as alternative representation of a gene, is adopted instead of using the standard alphabet sequence of bases A, T, C, G, U . After a general discussion on the use of supersymmetry in biological systems, we give examples of the use of supersymmetry for living organism, discuss the codon and anti-codon ghost fields and develop an algebraic construction for the trash DNA, the DNA area which does not seem active in biological systems. As a general result, all hidden states of codon can be computed by Chern-Simons 3 forms. Finally, we plot a time series of genetic variations of viral glycoprotein gene and host T-cell receptor gene by using a gene tensor correlation network related to the Chern-Simons current. An empirical analysis of genetic shift, in host cell receptor genes with separated cluster of gene and genetic drift in viral gene, is obtained by using a tensor correlation plot over time series data derived as the empirical mode decomposition of Chern-Simons current.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Chern‐Simons Current in Systems of DNA‐RNA Transcriptions

et al. 2018

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“…It might be a source of feedback loop in docking and undocking states in protein-protein interaction between V3 loop and CD4 protein receptor with its two co-receptors on the surface of CD4 cell. The situation is an analogy with Yang-Mills field in support spinor machine with coupling eight states in the physiology of Kolmogorov space [41] underlying time series data. The predictant orbital can be a spin in reverse direction of predictor orbital.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Laurent Polynomial in the Chern–Simons Current of V3 Loop Time Series

2019

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The genetic variation in the V3 loop obstruction component of gp120 HIV glycoprotein is defined as a solitary standing wave function with the transition state between the interaction of wave function in the DNA and RNA hidden states. It is a parasitism co-state generated from the coupling between two pairs of hidden 8 states of Laurent polynomial in the Chern-Simons current of HIV genotypes. A modified Nahm equation for a biology with Lax pairs is defined as the pairs of viral glycoprotein states with the transition between DNA and RNA in the reverse transcription process. The direction in the spinor field with genetic variation in 30 samples of the V3 loop time series with hidden 8 states of Laurent polynomial over the Chern-Simons current is predicted using a support spinor machine and convolutional neural network based on the imaging technique generated from tensor correlation network.We assume that the transcription process from the DNA to RNA and to a protein is a chain with the exact sequence in cohomology theory for biology. We cannot separate the DNA from RNA and the protein. It also happens in the transition state, which means that every state of an amino acid is a mixed state. One amino acid is linked with the other amino acids with an affine Poincare group with modulo SO(3, 1). [40] The derivation of a distribution in gene expression shows us the coupling transition protein states as an interaction of

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“…Some statisticians and economists typically predicted macroeconomics of the time series and the financial time series by using statistical data analysis of, ARIMA, ARCH, GARCH and the Markov switching model using the assumption of the linearity and the stationary time series process which cannot be found in the most of the typical financial time series data. The main defect of the ordinary least square (OLS) [29] ARIMA and GARCH model is based on fitting the problem with the parameters of fitting or learning with a single stochastic process for infinite factors which is governed by an infinite stochastic process influence on the future expectation price. When we add one point of the future price and fit curve by using the data mining tool for the regression with GARCH (1,1), the coefficient of the equation which we used to describe the historical data will update and change the historical path so that it makes a non-realistic situation.…”

Section: Introductionmentioning

confidence: 99%

GARCH(1,1) Model of the Financial Market with the Minkowski Metric

Pinčák

Kanjamapornkul

2018

Zeitschrift Für Naturforschung A

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We solved a stylized fact on a long memory process of volatility cluster phenomena by using Minkowski metric for GARCH(1,1) under assumption that price and time can not be separated. We provide a Yang-Mills equation in financial market and anomaly on superspace of time series data as a consequence of the proof from the general relativity theory. We used an original idea in Minkowski spacetime embedded in Kolmogorov space in time series data with behavior of traders. The result of this work is equivalent to the dark volatility or the hidden risk fear field induced by the interaction of the behavior of the trader in the financial market panic when the market crashed.

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Kolmogorov space in time series data

Cited by 20 publications

References 31 publications

The Chern‐Simons Current in Systems of DNA‐RNA Transcriptions

The Chern‐Simons Current in Systems of DNA‐RNA Transcriptions

Forecasting Laurent Polynomial in the Chern–Simons Current of V3 Loop Time Series

GARCH(1,1) Model of the Financial Market with the Minkowski Metric

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