Finding Periodic Regularities on Sequential Data: Converging, Diverging and Cyclic Patterns

Temporal information has become one of the most important features when it comes to data analytics. The need to understand the dynamics and evolutionary behaviors of different domains has driven data analytics' processes to make use of the temporal information associated with the data. Therefore, several approaches have been proposed, in the field of Temporal Pattern Mining, in order to use this temporal information to disclose temporal trends that could help in the decision making process. However, there are still significant limitations regarding both the quality of the disclosed information or the efficiency of the processes. In this work we propose a new constraint-based sequential mining method, called ConstraintPrefixSpan, for mining three types of periodic regularities: Cyclic, Converging and Diverging. Our experiments on two different datasets show both the quality of patterns found and the efficiency and flexibility of or algorithm to deal with multiple types of regularities.