skip to main content
10.1145/3653644.3658510acmotherconferencesArticle/Chapter ViewAbstractPublication PagesfaimlConference Proceedingsconference-collections
research-article

Financial Data Analysis and Prediction Based on Generative Adversarial Fill-in Network Optimisation

Published: 20 September 2024 Publication History

Abstract

Abstract. The analysis and prediction of financial data is crucial for investors and financial institutions, however, there are often a large number of missing values in financial data, which can seriously affect the integrity of the data and the accuracy of the analysis results. Therefore, the study proposes a financial data analysis and prediction method with improved generative adversarial filler network optimisation, which utilises the powerful generative capability of generative adversarial networks to fill in the financial data value that is missed, and combines it with traditional data analysis and prediction models to achieve more accurate and comprehensive analysis and prediction of the financial market. The experimental results show that the average absolute error of the proposed model under study is in the range of 0.1 to 0.11, and the performance of the model decreases less with the increase of the missing rate, and especially performs well at high missing rates. Regardless of the change in the number of attributes, the error value of the proposed model is still very small, which indicates that the model is also highly adaptable to handle data with different numbers of attributes. The proposed method can significantly improve the prediction accuracy and stability when dealing with financial data containing missing values. The study provides new ideas and technical support for the field of financial data analysis and prediction.

References

[1]
Ma Q, Jiang L, Yu W. Lambertian-based adversarial attacks on deep-learning-based underwater side-scan sonar image classification.Pattern Recognition: the Journal of the Pattern Recognition Society, 2023,138(1):109363-109382.
[2]
Liu W, Caoliu C J, Ren C, Wei Y, Guo H. Fine-grained Image Inpainting with Scale-Enhanced Generative Adversarial Network.Pattern Recognition Letters,. 2021, 143(8):81-87.
[3]
Dewi C, Chen R C, Liu Y T .Synthetic Traffic Sign Image Generation Applying Generative Adversarial Networks.Vietnam Journal of Computer Science, 2022, 9 (2):333-348.
[4]
Babu K K, Dubey S R .CDGAN: Cyclic Discriminative Generative Adversarial Networks for image-to-image transformation.Academic Press, 2022, 82(Jan.). 1-10.
[5]
Yu H .An Effective Model for Forecasting Travel Consumer Demand Using Big Data Analysis.Advances in Data Science and Adaptive Analysis, 2022, 14(1/2):1 -21.
[6]
Funama Y, Oda S, Kidoh M, Nagayama Y, Nakaura T. Conditional generative adversarial networks to generate pseudo low monoenergetic CT image from a single- tube voltage CT scanner.Physica Medica, 2021, 83(5):46-51.
[7]
Cheng W, Cheng R, Liu J, Ma W, Li J, Guan W. Multidimensional Intelligent Distribution Network Load Analysis and Forecasting Management System Based on Multidata Fusion Technology.Mathematical Problems in Engineering, 2021, 2021(7):1-24.
[8]
Dael F A, Yavuz Ö Ç, Yavuz U. Stock Market Prediction Using Generative Adversarial Networks (GANs): Hybrid Intelligent Model. Comput. Syst. Sci. Eng., 2023, 47(1): 19-35.
[9]
Li L, Wang B, Feng P, Wang H, Yu Q. Crop yield forecasting and associated optimum lead time analysis based on multi-source environmental data across China. Agricultural and Forest Meteorology, 2021, 308/309(4):108558-108580.
[10]
Bai T, Zhao J, Zhu J,Han S, Chen J, Li B. Toward Efficiently Evaluating the Robustness of Deep Neural Networks in IoT Systems: a GAN-Based Method.IEEE internet of things journal, 2022, 9(3):1875-1884.
[11]
Lan J, Zhang R, Yan Z, Wang J, Chen Y, Hou R. Adversarial attacks and defenses in Speaker Recognition Systems: a survey.Journal of systems architecture,. Journal of systems architecture, 2022, 127(1):102526-102538.
[12]
Zhu Q X, Xu T X, Xu Y .Improved Virtual Sample Generation Method Using Enhanced Conditional Generative Adversarial Networks with Cycle Structures for Soft Sensors with Limited Data.Industrial & Engineering Chemistry Research, 2022, 61(1):530-540.
[13]
Ferreira J P M, Coutinho T M, Gomes T L, José F N, Azevedo R, Martins R.Learning to dance: a graph convolutional adversarial network to generate realistic dance motions from audio.Pergamon, 2021, 94(Feb.):11-21.
[14]
Antonio Emanuele Ciná, Torcinovich A, Pelillo M .A Black-box Adversarial Attack for Poisoning Clustering.Pattern Recognition, 2021, 122(10). 108306-108317.
[15]
Marquez B Y, Realyvásquez-Vargas A, Lopez-Esparza N, Ramos C E. Application of ordinary least squares regression and neural networks in predicting employee turnover in the industry. Archives of Advanced Engineering Science, 2024, 2(1): 30-36.
[16]
Zhang J, Qian W, Nie R, Cao J, Xu D. Generate adversarial examples by adaptive moment iterative fast gradient sign method.Applied Intelligence: the International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies, 2023, 53(1):1101-1114.

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
FAIML '24: Proceedings of the 2024 3rd International Conference on Frontiers of Artificial Intelligence and Machine Learning
April 2024
379 pages
ISBN:9798400709777
DOI:10.1145/3653644
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 September 2024

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. Data analysis and prediction
  2. Financial data
  3. Generative adversarial filler networks
  4. Missing data filling

Qualifiers

  • Research-article
  • Research
  • Refereed limited

Conference

FAIML 2024

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 8
    Total Downloads
  • Downloads (Last 12 months)8
  • Downloads (Last 6 weeks)1
Reflects downloads up to 16 Feb 2025

Other Metrics

Citations

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format.

HTML Format

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media