The string prediction models as invariants of time series in the forex market

Pinčák, Richard

doi:10.1016/j.physa.2013.07.048

Cited by 16 publications

(13 citation statements)

References 22 publications

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“…In the works [14,15] the q-deformed prediction model based on the deviations from benchmark string sequence of 1-endpoint string map P (1) q (τ, h) was thoroughly studied. The momentum M of the string (the predictor) were proposed for the study of deviations of string maps from benchmark string sequence in the form…”

Section: From Simple To Complex Stringsmentioning

confidence: 99%

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Identification of market trends with string and D2-brane maps

Bartoš

Pinčák

2017

Physica A: Statistical Mechanics and its Applications

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The multi dimensional string objects are introduced as a new alternative for an application of string models for time series forecasting in trading on financial markets. The objects are represented by open string with 2-endpoints and D2-brane, which are continuous enhancement of 1-endpoint open string model. We show how new object properties can change the statistics of the predictors, which makes them the candidates for modeling a wide range of time series systems. String angular momentum is proposed as another tool to analyze the stability of currency rates except the historical volatility. To show the reliability of our approach with application of string models for time series forecasting we present the results of real demo simulations for four currency exchange pairs.Comment: 10 pages, 8 figures, 3 table

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Section: From Simple To Complex Stringsmentioning

confidence: 99%

“…We work on the concept which approach the string theory [13] to the field of time series forecast and data analysis through a transformation of currency rate data to the topology of physical strings and branes [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Identification of market trends with string and D2-brane maps

Bartoš

Pinčák

2017

Physica A: Statistical Mechanics and its Applications

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“…Here, we introduce a new representation of the genetic code in the time series for a string and a D-brane [48][49][50][51] modeling by applying a spinor field to a superspace in a time series data [52,53] over the viral glycoprotein [54] and a viral replication gene [55]. The method allows to develop supersymmetry for living organisms.…”

Section: Introductionmentioning

confidence: 99%

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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“…It is selected as a mathematical object to classify a feature space in the space of time series data. This fact gives us a new theory of quantum probability over hyperbolic number [20], and an extradimension approach [21][22][23][24] for time series data prediction. We observe that there exists some algebraic topological defect in the mathematical structure of machine learning for support vector classification.…”

Section: Introductionmentioning

confidence: 99%

Support spinor machine

Kanjamapornkul

Pinčák

Chunithipaisan

et al. 2017

Digital Signal Processing

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We generalize a support vector machine to a support spinor machine by using the mathematical structure of wedge product over vector machine in order to extend field from vector field to spinor field. The separated hyperplane is extended to Kolmogorov space in time series data which allow us to extend a structure of support vector machine to a support tensor machine and a support tensor machine moduli space. Our performance test on support spinor machine is done over one class classification of end point in physiology state of time series data after empirical mode analysis and compared with support vector machine test. We implement algorithm of support spinor machine by using Holo-Hilbert amplitude modulation for fully nonlinear and nonstationary time series data analysis.

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The string prediction models as invariants of time series in the forex market

Cited by 16 publications

References 22 publications

Identification of market trends with string and D2-brane maps

Identification of market trends with string and D2-brane maps

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

Support spinor machine

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