Co-occurrence order-preserving pattern mining

TL;DR

This paper introduces COP-Miner, an efficient algorithm for co-occurrence order-preserving pattern mining in time series, focusing on trend-specific patterns and demonstrating superior performance over existing methods.

Contribution

The paper proposes COP-Miner, a novel algorithm that efficiently discovers co-occurrence order-preserving patterns related to specific trends in time series data.

Findings

COP-Miner outperforms competing algorithms in speed and scalability.

COPs with keypoint alignment improve trend prediction accuracy.

The method effectively extracts trend-related patterns from large time series datasets.

Abstract

Recently, order-preserving pattern (OPP) mining has been proposed to discover some patterns, which can be seen as trend changes in time series. Although existing OPP mining algorithms have achieved satisfactory performance, they discover all frequent patterns. However, in some cases, users focus on a particular trend and its associated trends. To efficiently discover trend information related to a specific prefix pattern, this paper addresses the issue of co-occurrence OPP mining (COP) and proposes an algorithm named COP-Miner to discover COPs from historical time series. COP-Miner consists of three parts: extracting keypoints, preparation stage, and iteratively calculating supports and mining frequent COPs. Extracting keypoints is used to obtain local extreme points of patterns and time series. The preparation stage is designed to prepare for the first round of mining, which contains four steps: obtaining the suffix OPP of the keypoint sub-time series, calculating the occurrences of the suffix OPP, verifying the occurrences of the keypoint sub-time series, and calculating the occurrences of all fusion patterns of the keypoint sub-time series. To further improve the efficiency of support calculation, we propose a support calculation method with an ending strategy that uses the occurrences of prefix and suffix patterns to calculate the occurrences of superpatterns. Experimental results indicate that COP-Miner outperforms the other competing algorithms in running time and scalability. Moreover, COPs with keypoint alignment yield better prediction performance.