Abstract
This article considers the gene ranking algorithm for the microarray data. The rank vector is estimated by classifications of the random data samples. At each iteration, the ranks of genes participating in the successful classification become higher. Unlike other methods of feature selection, the proposed algorithm allows increasing the generality of the classification models by construction of the balanced training samples and taking into account the descriptiveness of the gene combinations by the subset estimation.
Highlights
This paper continues the series of papers [2]–[5] devoted to the volatility analysis of financial time series – stock market data
This paper explores an alternative volatility estimation approach discovering the helical structure of Fourier coefficients of volatility wave
We have found some regularity in the volatility evolution process
Summary
This paper continues the series of papers [2]–[5] devoted to the volatility analysis of financial time series – stock market data. Since wavelet decomposition uses the shifted and scaled mother wavelet function, it acts like a microscope, highlighting certain parts of the signal These specific properties were described in [1]–[4] with the author term “North-East Volatility Wind Effect”. The main conclusion made in paper [2] is the following – a slight increase in volatility in the low-frequency components of the signal leads to significant disturbances in highfrequency components that destine the entire signal volatility growth This effect is called “North-East Volatility Wind”. “North-East Volatility Wind” Effect described in [2] brings out a deeper understanding of volatility evolution and an opportunity to illuminate most dramatically market drawdowns initially This opportunity is explained by an ability to see very small changes in volatility (logarithmic variance) of the low-frequency components of the signal.
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