Recent Advances in Data Science and Symmetry in AI: Theory and Applications

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 443

Special Issue Editors

Dr. Diyin Tang
E-Mail Website
Guest Editor
School of Automation Science and Electrical Engineering, Beihang University, Beijing, China
Interests: condition monitoring
Dr. Danyang Han
E-Mail Website
Guest Editor
School of Automation Science and Electrical Engineering, Beihang University, Beijing, China
Interests: condition monitoring

Special Issue Information

Dear Colleagues,

As data plays a key role in the development of theories and applications in artificial intelligence, the focus of AI has been transferred from model-central research to data-central research. In recent years, there has been an increasing interest in AI to deal with data issues including unbalanced samples, small samples and low-quality samples. For example, the AI method for fault diagnosis under unbalanced samples has been investigated, which provides less health information and decreases the accuracy of anomaly detection. Despite numerous works on related topics published in various journals and academic forums, some key issues related to the topic of data science and symmetry in AI remain unexplored and unaddressed.

Therefore, the aim of this Special Issue is to present advanced research on both theories and applications of artificial intelligence in dealing with data issues, especially regarding the design of efficient and effective data analysis models, algorithms and systems to improve reasoning and treatment.

We are soliciting contributions (research and review articles) covering a broad range of topics regarding the symmetry and asymmetry in data Science and symmetry in AI, including, though not limited to, the following:

  1. Data processing in artificial intelligence;
  2. Feature engineering in artificial intelligence;
  3. Applications of artificial intelligence for data issues;
  4. Improved artificial intelligence algorithms for data issues;
  5. A development review of the data science in AI.

Dr. Diyin Tang
Dr. Danyang Han
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Symmetry is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • data processing
  • intelligent data analysis
  • feature engineering

Published Papers (1 paper)

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Research

17 pages, 1801 KiB  
Article
Enhancing Portfolio Performance through Financial Time-Series Decomposition-Based Variational Encoder-Decoder Data Augmentation
Symmetry 2024, 16(3), 283; https://doi.org/10.3390/sym16030283 - 29 Feb 2024
Viewed by 225
Abstract
The objective of portfolio diversification is to reduce risk and potentially enhance returns by spreading investments across different asset classes. Existing portfolio diversification models have traditionally been trained on historical financial time series data. However, several issues arise with historical financial time series [...] Read more.
The objective of portfolio diversification is to reduce risk and potentially enhance returns by spreading investments across different asset classes. Existing portfolio diversification models have traditionally been trained on historical financial time series data. However, several issues arise with historical financial time series data, making it challenging to train models effectively to achieve the portfolio diversification objective: an insufficient amount of training data and the uncertainty deficiency problem, wherein the uncertainty that existed in the past is not visible in the present. Insufficient datasets, characterized by small data size, result in information asymmetry and compromise portfolio performance. This limitation underscores the importance of adopting a pattern-centric data augmentation approach, capable of unveiling hidden patterns and structures within the financial time series data. To address these challenges, this paper introduces the financial time series decomposition-based variational encoder-decoder (FED) method to augment financial time series data, overcoming the limitations of insufficient training data and providing a more realistic and dynamic simulation of the financial market environment. By decomposing the data into distinct components, such as trend, dispersion, and residual, FED leverages pattern-centric data augmentation within the financial time series data. In the environment generated using the FED method, this paper proposes a two-class portfolio diversification, called FED2Port. It integrates stochastic elements into the reward function, enabling a reinforcement learning algorithm to learn from a comprehensive spectrum of financial market uncertainties. The experimental results demonstrate that the proposed model significantly enhances portfolio performance. Full article
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