Mining relevant partial periodic pattern of multi-source time series data

Traditional partial periodic pattern mining algorithms tend to work on a single time series or database. However, time series databases usually consist of interrelated multivariate time series from different sources in real application scenarios. A new pattern-relevant partial periodic patterns and its corresponding mining algorithm (PMMS-Eclat) are designed, which can effectively reflect and mine the correlations of multi-source time series data. And PMMS-Eclat introduces the contribution difference of various patterns in the mining process to quickly identify important patterns. Firstly, PMMS-Eclat, an improved mining algorithm based on Ecalt, is used to obtain frequent partial periodic patterns from single time serie, which avoids the expensive tree structure adjustment cost. In this process, two new metrics are defined to characterize the correlation among sequential data effectively, namely density ratio and average periodic rate, which respectively reflect the density and occurrence rate of patterns. Secondly, the frequent partial periodic patterns excavated above are used as input matrices, and the ultimate relevant partial periodic patterns are obtained based on LSH principle. Finally, experiments on various datasets are conducted to verify the effectiveness of our proposed algorithm, and the results show that PMMS-Eclat is more competent for sparse databases.(c) 2022 Elsevier Inc. All rights reserved.

Automated summary

Traditional partial periodic pattern mining algorithms tend to work on a single time series or database. This article proposes a new pattern-relevant partial periodic patterns and its mining algorithm, which can effectively reflect and mine the correlations of multi-source time series data. The algorithm quickly identifies important patterns by introducing the contribution difference of various patterns, and utilizes an improved mining algorithm to obtain frequent partial periodic patterns. Experimental results demonstrate the effectiveness of the algorithm in sparse databases.