Open AccessPOSSIBILITIES OF PERFORMING BANKRUPTCY DATA ANALYSIS USING TIME SERIES CLUSTERING
Author(s) -
Peter Grabusts
Publication year - 2010
Publication title -
latgale national economy research/latgales tautsaimniecības pētījumi
Language(s) - English
Resource type - Journals
eISSN - 2256-0955
pISSN - 1691-5828
DOI - 10.17770/lner2010vol1.2.1779
Subject(s) - cluster analysis , bankruptcy , computer science , bankruptcy prediction , data mining , time series , novelty , linear discriminant analysis , multivariate statistics , artificial intelligence , econometrics , machine learning , mathematics , finance , economics , psychology , social psychology
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.