Sliding window based weighted periodic pattern mining over time series data
Rizvee, Redwan Ahmed
Shahin, Md Shahadat Hossain
Ahmed, Chowdhury Farhan
Leung, Carson K.
Mai, Jiaxing Jason
Sliding windows have been crucial in mining time series. Many existing studies focus on reconstruction of the underlying structure (e.g., suffix tree) for each new window. However, when the window size is large or when the window slides frequently, reconstruction may perform poorly. In this paper, we propose a solution that dynamically updates the structure (rather than reconstruction for each modification or sliding). Moreover, many existing studies rely on the weight of maximum weighted item in the database to avoid testing unnecessary patterns when mining weighted periodic patterns from time series, but it may still require lots of weight checking to determine whether a pattern is a candidate. In this paper, we also propose an additional solution to address this problem by discarding unimportant patterns beforehand so as to speed up the candidate generation process. Evaluation results on real-life datasets show the effectiveness of our two solutions in handling sliding window and pruning redundant candidate patterns.
Time series, Weighted periodic pattern mining, Dynamic database, Sliding window, Pruning
Rizvee, R.A., Shahin, M.S.H., Ahmed, C.F., Leung, C.K., Deng, D., Mai, J.J.: Sliding window based weighted periodic pattern mining over time series data. In: ICDM 2019, pp. 118-132 (2019)