Knowledge discovery in time series databases.
IEEE Trans Syst Man Cybern B Cybern
; 31(1): 160-9, 2001.
Article
em En
| MEDLINE
| ID: mdl-18244779
ABSTRACT
Adding the dimension of time to databases produces time series databases (TSDB) and introduces new aspects and difficulties to data mining and knowledge discovery. In this correspondence, we introduce a general methodology for knowledge discovery in TSDB. The process of knowledge discovery in TSDR includes cleaning and filtering of time series data, identifying the most important predicting attributes, and extracting a set of association rules that can be used to predict the time series behavior in the future. Our method is based on signal processing techniques and the information-theoretic fuzzy approach to knowledge discovery. The computational theory of perception (CTP) is used to reduce the set of extracted rules by fuzzification and aggregation. We demonstrate our approach on two types of time series stock-market data and weather data.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Idioma:
En
Revista:
IEEE Trans Syst Man Cybern B Cybern
Ano de publicação:
2001
Tipo de documento:
Article