Analysis of Time Series Rule Extraction Techniques

Abstract

In Data mining, the sequence of data points are measured typically at successive time instants spaced at uniform time intervals are called time series. The real applications of time series are frequent pattern analysis, bioinformatics, medical treatment, meteorology, sociology and economics. Frequent patterns can be analyzed to give explanatory rules and this rule extraction can be done using many algorithms like Genetic Algorithm, Fuzzy Logic , Support Vector Machine etc. Rule induction is an area of machine learning in which rules are extracted from collective set of observations. The rules extracted may represent complete scientific model of the data, or simply represent local patterns in the data. A brief overview of some of the most common rule extraction techniques and a comparison between single and hybrid rule approaches comprise in this survey.