Discovering Periodicity in Locally Repeating Patterns


Our latest research article, “Discovering Periodicity in Locally Repeating Patterns” has been accepted to the 9th IEEE International Conference on Data Science and Advanced Analytics. We address the problem of efficiently identifying and analysing business account transactions. Existing approaches often require a target period to be specified, however in this paper, we extend one such approach, derived from frequent pattern mining, to operate without the need for user-specified periodicity. Experimental results of our implementation show that the new approach can identify many more patterns in a real-world financial dataset, while on other sequential datasets it finds similar numbers of patterns without significant reduction in efficiency compared to existing approaches.

To read the full paper, click the PDF link below.

Keep posted on our LinkedIn.

Contribute to the discussion on the future of AI and lending