Rule generation for categorical time series with Markov assumptions
Several procedures of sequential pattern analysis are designed to detect frequently occurring patterns in a single categorical time series (episode mining). Based on these frequent patterns, rules are generated and evaluated, for example, in terms of their confidence. The confidence value is commonly interpreted as an estimate of a conditional probability, so some kind of stochastic model has to be assumed. The model is identified as a variable length Markov model. With this assumption, the usual confidences are maximum likelihood estimates of the transition probabilities of the Markov model. We discuss possibilities of how to efficiently fit an appropriate model to the data. Based on this model, rules are formulated. It is demonstrated that this new approach generates noticeably less and more reliable rules.
categorical time series sequential pattern analysis episode mining variable length markov model