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<p>In this paper, after analyzing the existing independent component analysis algorithms, the high-dimensional high-frequency data are preprocessed by whitening, and the objective functions of crag, negative entropy, approximate negative entropy, and mutual information are set. The independent component analysis model is designed to separate the independence between signals by maximizing non-Gaussianity, estimating great likelihood, and minimizing mutual information measures. In order to verify that the dimensionality reduction technique based on independent component analysis can effectively extract high-dimensional, high-frequency data information, it is applied to the industry’s closing index data and financial market data. The results show that the stock price synthesized using the six major independent factors is almost the same as the original stock price trend of DG, and the difference in stock price evaluation is within 5, indicating that the six major independent factors play a decisive role in the stock price trend. The study shows that the dimensionality reduction technique based on independent component analysis can analyze the volatility of stock prices and obtain more effective information from high-dimensional, high-frequency data.</p>
</abstract>

In this paper, after analyzing the existing independent component analysis algorithms, the high-dimensional high-frequency data are preprocessed by whitening, and the objective functions of crag, negative entropy, approximate negative entropy, and mutual information are set. The independent component analysis model is designed to separate the independence between signals by maximizing non-Gaussianity, estimating great likelihood, and minimizing mutual information measures. In order to verify that the dimensionality reduction technique based on independent component analysis can effectively extract high-dimensional, high-frequency data information, it is applied to the industry’s closing index data and financial market data. The results show that the stock price synthesized using the six major independent factors is almost the same as the original stock price trend of DG, and the difference in stock price evaluation is within 5, indicating that the six major independent factors play a decisive role in the stock price trend. The study shows that the dimensionality reduction technique based on independent component analysis can analyze the volatility of stock prices and obtain more effective information from high-dimensional, high-frequency data.

Application of independent component analysis-based dimensionality reduction technique in effective information extraction of high-dimensional high-frequency data

Tianjin University of Technology and Education

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

In this paper, after analyzing the existing independent component analysis algorithms, the high-dimensional high-frequency data are preprocessed by whitening,...

Application of independent component analysis-based dimensionality reduction technique in effective information extraction of high-dimensional high-frequency data

Online veröffentlicht: 13. Dez. 2023

Seitenbereich: -

Eingereicht: 09. Feb. 2023

Akzeptiert: 23. Juni 2023

DOI: https://doi.org/10.2478/amns.2023.2.01478

Schlüsselwörter
Independent component analysis, Whitening, Objective function, Great likelihood estimation, High-dimensional high-frequency data

© 2023 Xiaohui Wang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Application of independent component analysis-based dimensionality reduction technique in effective information extraction of high-dimensional high-frequency data

Online veröffentlicht: 13. Dez. 2023

Seitenbereich: -

Eingereicht: 09. Feb. 2023

Akzeptiert: 23. Juni 2023

DOI: https://doi.org/10.2478/amns.2023.2.01478

SchlüsselwörterIndependent component analysis, Whitening, Objective function, Great likelihood estimation, High-dimensional high-frequency data

© 2023 Xiaohui Wang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Independent component analysis, Whitening, Objective function, Great likelihood estimation, High-dimensional high-frequency data