Repository logo
  • English
  • Latviešu
  • Log In
    or
    New user? Click here to register.Have you forgotten your password?
Repository logo
  • Communities & Collections
  • Research Outputs
  • Projects
  • People
  • English
  • Latviešu
  • Log In
    or
    New user? Click here to register.Have you forgotten your password?
  1. Home
  2. Faculty of Engineering
  3. Scientific publications
  4. Scientific papers (IF)
  5. POSSIBILITIES OF PERFORMING BANKRUPTCY DATA ANALYSIS USING TIME SERIES CLUSTERING
 
  • Details
Options

POSSIBILITIES OF PERFORMING BANKRUPTCY DATA ANALYSIS USING TIME SERIES CLUSTERING

Journal
Latgale National Economy Research: Journal of Social Sciences
ISSN
1691-5828
Date Issued
2010
Author(s)
Grabusts, Pēteris 
Rezekne Academy of Technologies 
DOI
10.17770/lner2010vol1.2.1779
Abstract
Prediction of corporate bankruptcy is a study topic of great interest.Under the conditions of the modern free market, early diagnostics of unfavourabledevelopment trends of company’s activity or bankruptcy becomes a matter ofgreat importance. There is no general method which would allow one to forecastunfavourable consequence with a high confidence degree. This paper focuses onthe analysis of the approaches that can be used to perform an early bankruptcydiagnostics- in previous research multivariate discriminant analysis (MDA), neuralnetwork based approach and rule extraction method have been examined. Lately,time series clustering approach has become popular and its feasibility forbankruptcy data analysis is being investigated. Experiments carried out validatethe use of such methods in the given class of tasks. As a novelty, an attempt toapply time series clustering method to the analysis of bankruptcy data is made.
Subjects
  • bankruptcy prediction...

  • financial ratio

  • time series

  • clustering

File(s)
 POSSIBILITIES OF PERFORMING BANKRUPTCY DATA ANALYSIS USING TIME SERIES CLUSTERING.pdf (236.95 KB)
Scopus© citations
0
Acquisition Date
Jan 12, 2024
google-scholar
Views
Downloads
User Guide
  • Documentation

© Rezekne Academy of Technologies

Built with DSpace-CRIS software - Extension maintained and optimized by 4Science

  • Cookie settings
  • Privacy policy
  • End User Agreement
  • Send Feedback