Mining Calendar-Based Periodicities of Patterns in Temporal Data

摘要

An efficient algorithm with a worst-case time complexity of O(n log n) is proposed for detecting seasonal (calendar-based) periodicities of patterns in temporal datasets. Hierarchical data structures are used for representing the timestamps associated with the data. This representation facilitates the detection of different types of seasonal periodicities viz. yearly periodicities, monthly periodicities, daily periodicities etc. of patterns in the temporal dataset. The algorithm is tested with real-life data and the results are given.