Fuzzy knowledge discovery from time series data for events prediction

Abstract

When the time dimension is added to datasets, time series data are obtained. Extracting knowledge from time series data requires special attention to the timing aspects of the data. An interesting activity in the field of knowledge discovery from time series data is predicting the timing of upcoming events. In this paper we present a method for mining fuzzy knowledge from time series data. In contrast to traditional time series analysis methods which largely focus on global models, our method is about the discovery of local patterns in time series. The extracted knowledge will be in the form of fuzzy association rules and it aims at predicting the approximate timing of upcoming events. The proposed method includes cleaning and filtering of time series data, segmenting time series, extracting important features for prediction, further cleaning on feature values, fuzzifying feature values, extracting fuzzy association rules, and pruning the discovered rules. We will show the efficiency of our approach on a stock market dataset. © 2008 Springer Berlin Heidelberg.