Knowledge discovery in time series databases

作     者：Last, M Klein, Y Kandel, A 

作者机构：Univ S Florida Dept Comp Sci & Engn Tampa FL 33620 USA Tel Aviv Univ Dept Elect Engn Syst IL-69978 Tel Aviv Israel 

出 版 物：《IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS》 (IEEE Trans Syst Man Cybern Part B Cybern)

年 卷 期：2001年第31卷第1期

页      面：160-169页

核心收录：

主　　题：computational theory of perception data mining fuzzy association rules knowledge discovery in databases time series databases 

摘      要：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 TSDB 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 an two types of time series: stock-market data and weather data.