Sequence Prediction using Partially-Ordered Episode Rules

Predicting the next event (or symbol) of a sequence has many applications such as network event prediction, webpage prefetching, activity recommendation and stock price prediction. An effective approach for event prediction that has the benefit of being interpretable, is to extract episode rules to then apply them for prediction. This paper improves upon this approach by using a novel type of episode rules called partially-ordered episode rules. These rules are more general than standard episode rules as they loosen the ordering constraint on events in each rule antecedent and consequent. Hence, a partially-ordered episode rule can replace multiple standard episode rules. Experiments on several benchmark datasets show that partially-ordered episode rules can provide more accurate predictions than standard episode rules.