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Symmetry
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Symmetry and Asymmetry in Data Analysis
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Symmetry and Asymmetry in Data Analysis

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

Deadline for manuscript submissions: 31 August 2025 | Viewed by 1157

Special Issue Editors

Prof. Dr. Song Deng

E-Mail Website
Guest Editor
Institute of Advanced Technology, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
Interests: data security; privacy preservation;cyber-physical system;data mining; big data
Prof. Dr. Xiong Fu

E-Mail Website
Guest Editor
School of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
Interests: data analysis; cloud computing
Prof. Dr. Di Wu

E-Mail Website
Guest Editor
College of Computer and Information Science, Southwest University, Chongqing, China
Interests: big data; recommender systems; environmental protection
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the realm of data analysis, symmetry and asymmetry are pivotal concepts that offer profound insights into the structure and distribution of data. Symmetry in data often implies a balance or uniformity, which can be indicative of a stable or predictable system. It is frequently observed in well-behaved datasets, where the distribution of data points is even across different categories or variables. This property can simplify analysis by allowing analysts to apply symmetrical models, such as the normal distribution, to make predictions or inferences with a high degree of confidence.

Conversely, asymmetry in data introduces complexity and variability, which can be challenging to model but also rich in information. Asymmetric data distributions, such as skewed or bimodal patterns, suggest underlying processes that may not conform to simple assumptions of symmetry. Analyzing asymmetry requires more nuanced approaches, such as using non-parametric tests or transforming data to better fit symmetrical models. The presence of asymmetry can reveal hidden patterns, outliers, or the influence of confounding variables that might be overlooked in a symmetrical analysis.

Prof. Dr. Song Deng
Prof. Dr. Xiong Fu
Prof. Dr. Di Wu
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 mining
  • machine learning
  • artificial intelligence
  • data analysis for industrial information
  • outlier identification
  • data distribution

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Published Papers (2 papers)

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Research

20 pages, 1956 KiB  
Article
Recommendation Model Based on Global Intention Learning and Sequence Augmentation
by Minghui Li, Wei Lu and Xiaodong Cai
Symmetry 2025, 17(4), 586; https://doi.org/10.3390/sym17040586 - 11 Apr 2025
Viewed by 277
Abstract
User interaction behavior is influenced by various intentions, which are often asymmetric. Incorporating intention information into sequential recommendation can significantly improve recommendation performance. However, most existing intention modeling methods rely on auxiliary information or random data augmentation to capture user intentions, which cannot [...] Read more.
User interaction behavior is influenced by various intentions, which are often asymmetric. Incorporating intention information into sequential recommendation can significantly improve recommendation performance. However, most existing intention modeling methods rely on auxiliary information or random data augmentation to capture user intentions, which cannot effectively capture the potential correlations between different user intentions, especially when dealing with asymmetric intentions. Furthermore, using random data augmentation methods may amplify the noise in the original sequence, leading to a decline in the model’s recommendation performance. To address these issues, this paper proposes a recommendation model based on Global Intention Learning and Sequence Augmentation. Firstly, a novel sequence information extraction module is designed, which efficiently integrates the refined global item association graph into item representations through a self-supervised approach, thereby capturing global collaborative sequence information. Secondly, an improved sequence augmentation strategy is adopted to reduce the disruption of the original item correlations, making the intention representation more accurate. Finally, intention information is integrated into the sequential recommendation model through a contrastive learning method, further enhancing the accuracy of the model’s recommendations. Experimental results show that compared to several state-of-the-art methods, the proposed model exhibits significant improvements on the Sports, Toys and LastFM datasets. Full article
(This article belongs to the Special Issue Symmetry and Asymmetry in Data Analysis)
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18 pages, 1412 KiB  
Article
Photovoltaic Power Prediction Technology Based on Multi-Source Feature Fusion
by Xia Zhou, Xize Zhang, Jianfeng Dai and Tengfei Zhang
Symmetry 2025, 17(3), 414; https://doi.org/10.3390/sym17030414 - 10 Mar 2025
Viewed by 509
Abstract
With the increase in photovoltaic installed capacity year by year, accurate photovoltaic power prediction is of great significance for photovoltaic grid-connected operation and scheduling planning. In order to improve the prediction accuracy, this paper proposes a photovoltaic power prediction combination model based on [...] Read more.
With the increase in photovoltaic installed capacity year by year, accurate photovoltaic power prediction is of great significance for photovoltaic grid-connected operation and scheduling planning. In order to improve the prediction accuracy, this paper proposes a photovoltaic power prediction combination model based on Pearson Correlation Coefficient (PCC), Complete Ensemble Empirical Mode Decomposition (CEEMDAN), K-means clustering, Variational Mode Decomposition (VMD), Convolutional Neural Network (CNN), and Bidirectional Long Short-Term Memory (BiLSTM). By making full use of the symmetric structure of the BiLSTM algorithm, one part is used to process the data sequence in order, and the other part is used to process the data sequence in reverse order. It captures the characteristics of sequence data by simultaneously processing a ‘symmetric’ information. Firstly, the historical photovoltaic data are preprocessed, and the correlation analysis of meteorological factors is carried out by PCC, and the high correlation factors are extracted to obtain the multivariate time series feature matrix of meteorological factors. Then, the historical photovoltaic power data are decomposed into multiple intrinsic modes and a residual component at one time by CEEMDAN. The high-frequency components are clustered by K-means combined with sample entropy, and the high-frequency components are decomposed and refined by VMD to form a multi-scale characteristic mode matrix. Finally, the obtained features are input into the CNN–BiLSTM model for the final photovoltaic power prediction results. After experimental verification, compared with the traditional single-mode decomposition algorithm (such as CEEMDAN–BiLSTM, VMD–BiLSTM), the combined prediction method proposed reduces MAE by more than 0.016 and RMSE by more than 0.017, which shows excellent accuracy and stability. Full article
(This article belongs to the Special Issue Symmetry and Asymmetry in Data Analysis)
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