OPP-Miner: Order-preserving sequential pattern mining

TL;DR

OPP-Miner introduces a novel order-preserving pattern mining method for time series data, capturing relative value trends without data conversion, enhancing pattern discovery, and demonstrating efficiency and practical utility in real-world applications like COVID-19 analysis.

Contribution

This paper presents OPP-Miner, the first algorithm for mining order-preserving patterns in time series, addressing limitations of existing methods by focusing on relative order rather than data values.

Findings

OPP-Miner is efficient and scalable for large datasets.

The method effectively discovers similar sub-sequences and trends.

Case studies show high utility in epidemic analysis and clustering improvement.

Abstract

A time series is a collection of measurements in chronological order. Discovering patterns from time series is useful in many domains, such as stock analysis, disease detection, and weather forecast. To discover patterns, existing methods often convert time series data into another form, such as nominal/symbolic format, to reduce dimensionality, which inevitably deviates the data values. Moreover, existing methods mainly neglect the order relationships between time series values. To tackle these issues, inspired by order-preserving matching, this paper proposes an Order-Preserving sequential Pattern (OPP) mining method, which represents patterns based on the order relationships of the time series data. An inherent advantage of such representation is that the trend of a time series can be represented by the relative order of the values underneath the time series data. To obtain frequent trends in time series, we propose the OPP-Miner algorithm to mine patterns with the same trend (sub-sequences with the same relative order). OPP-Miner employs the filtration and verification strategies to calculate the support and uses pattern fusion strategy to generate candidate patterns. To compress the result set, we also study finding the maximal OPPs. Experiments validate that OPP-Miner is not only efficient and scalable but can also discover similar sub-sequences in time series. In addition, case studies show that our algorithms have high utility in analyzing the COVID-19 epidemic by identifying critical trends and improve the clustering performance.