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Mining calendar-based periodicities of patterns in temporal data

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2009) 5909 LNCS 243-248
DOI: 10.1007/978-3-642-11164-8_39
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Abstract

An efficient algorithm with a worst-case time complexity of O(n logn) 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. © 2009 Springer-Verlag Berlin Heidelberg.

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Dutta, M., & Mahanta, A. K. (2009). Mining calendar-based periodicities of patterns in temporal data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5909 LNCS, pp. 243–248). https://doi.org/10.1007/978-3-642-11164-8_39

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