Mining Nonambiguous Temporal Patterns for Interval-Based Events

TLDR

Sequential pattern mining has largely focused on point-based events, while interval-based event data have been underexplored; existing interval temporal relationships (e.g., Kam and Fu’s 13 relations) lead to ambiguous patterns that fail to capture true event relationships. This study introduces a new nonambiguous temporal pattern definition for interval-based event data. The authors develop the TPrefixSpan algorithm to mine these patterns and validate its applicability by extracting temporal patterns from historical Nasdaq data. The algorithm is proven complete and accurate, and experimental results demonstrate satisfactory efficiency, scalability, and effectiveness on Nasdaq data.

Abstract

Previous research on mining sequential patterns mainly focused on discovering patterns from point-based event data. Little effort has been put toward mining patterns from interval-based event data, where a pair of time values is associated with each event. Kam and Fu's work [31] in 2000 identified 13 temporal relationships between two intervals. According to these temporal relationships, a new variant of temporal patterns was defined for interval-based event data. Unfortunately, the patterns defined in this manner are ambiguous, which means that the temporal relationships among events cannot be correctly represented in temporal patterns. To resolve this problem, we first define a new kind of nonambiguous temporal pattern for interval-based event data. Then, the TPrefixSpan algorithm is developed to mine the new temporal patterns from interval-based events. The completeness and accuracy of the results are also proven. The experimental results show that the efficiency and scalability of the TPrefixSpan algorithm are satisfactory. Furthermore, to show the applicability and effectiveness of temporal pattern mining, we execute experiments to discover temporal patterns from historical Nasdaq data.