LEARNING SEQUENTIAL PATTERNS FOR PROBABILISTIC INDUCTIVE PREDICTION

Suppose we are given a sequence of events that are generated probabilistically in the sense that the attributes of one event are dependent, to a certain extent, on those observed before it. This paper presents an inductive method that is capable of detecting the inherent patterns in such a sequence and to make predictions about the attributes of future events. Unlike previous AI-based prediction methods, the proposed method is particularly effective in discovering knowledge in ordered event sequences even if noisy data are being dealt with. The method can be divided into three phases: (i) detection of underlying patterns in an ordered event sequence; (ii) construction of sequence-generation rules based on the detected patterns; and (iii) use of these rules to predict the attributes of future events. The method has been implemented in a program called OBSERVER-II, which has been tested with both simulated and real-life data. Experimental results indicate that it is capable of discovering underlying patterns and explaining the behaviour of certain sequence-generation processes that are not obvious or easily understood. The performance of OBSERVER-II has been compared with that of existing AI-based prediction systems, and it is found to be able to successfully solve prediction problems programs such as SPARC have failed on.