OUTO-Miner: Detecting outlying occurrences in maximal frequent order-preserving patterns in time series

Abstract

Order-preserving pattern (OPP) mining primarily focuses on the frequent trends of time series, and frequent OPPs have potential crucial value. However, the results of OPP mining ignore the significance of numerical values, especially in the field of outlier detection. In addition, OPP mining often generates redundant patterns, leading to high memory consumption or low operational efficiency in outlier detection. To address these problems, this paper focuses on detecting outlying occurrences (OUTO) in maximal frequent order-preserving patterns, which employs the dynamic time warping method to calculate the distance between two sub-time series, and proposes OUTO-Miner to detect outlying occurrences. In data preprocessing, a linear fitting method is employed to extract key points, compressing the data and preserving the main features. To mitigate the generation of redundant patterns, OUTO-Miner utilizes maximal frequent OPPs for outlier detection. To avoid excessive computations, OUTO-Miner uses the interquartile range method to identify sub-time series with a high probability of being an OUTO. To validate the performance of OUTO-Miner, 13 competitive algorithms and 17 datasets are selected. The results demonstrate that OUTO-Miner outperforms all competitive algorithms in terms of runtime, memory consumption, and outlier detection. All algorithms can be downloaded from https://github.com/wuc567/Pattern-Mining/tree/master/OUTO-Miner.