Efficient Mining of Co-occurring Order-Preserving Patterns in Time Series

Core Concepts

This paper proposes an efficient algorithm, COP-Miner, to discover all frequent co-occurrence order-preserving patterns (COPs) in time series data, given a specific prefix pattern.

Abstract

The paper addresses the issue of co-occurrence order-preserving pattern (COP) mining in time series data. The key contributions are: Extracting keypoints from the original time series to reduce distortion and avoid mining redundant patterns. The preparation stage includes four steps: Obtaining the suffix order-preserving pattern (OPP) of the keypoint sub-time series. Calculating the occurrences of the suffix OPP. Verifying the occurrences of the keypoint sub-time series. Calculating the occurrences of all fusion patterns of the keypoint sub-time series. Proposing the concept of fusion pattern to effectively reduce the number of candidate patterns. Developing a support calculation method with an ending strategy that uses the occurrences of prefix and suffix patterns to calculate the occurrences of superpatterns, improving the efficiency of support calculation. Experimental results show that COP-Miner outperforms other competing algorithms in running time and scalability, and COPs with keypoint alignment yield better prediction performance.

Stats

The time series t has 19 data points. The keypoint time series k has 15 data points. The suffix OPP s = (1,3,2) has 5 occurrences in k. The prefix OPP o = (2,1,4,3) has 4 occurrences in k. There are 5 fusion patterns of o, with occurrences ranging from 5 to 15.

Quotes

"To avoid mining irrelevant trends and to obtain better prediction performance for time series, we explore COP mining, which can mine all COPs with the same prefix pattern, and we propose the COP-Miner algorithm." "COP-Miner is composed of three parts: extracting keypoints to reduce distortion interference and avoid mining redundant patterns, preparation stage to prepare for the first round of mining, and iteratively calculating supports of superpatterns and mining frequent COPs." "To further improve the efficiency of support calculation, we propose a support calculation method with an ending strategy which uses the occurrences of prefix and suffix patterns to calculate the occurrences of superpatterns."

Key Insights Distilled From

Co-occurrence order-preserving pattern mining

by Youxi Wu,Zhe... at arxiv.org 05-01-2024

https://arxiv.org/pdf/2404.19243.pdf

Deeper Inquiries

How can the COP-Miner algorithm be extended to handle time series with missing data or irregular sampling intervals

To extend the COP-Miner algorithm to handle time series with missing data or irregular sampling intervals, several modifications and enhancements can be implemented: Handling Missing Data: Introduce a data imputation step to fill in missing values in the time series before extracting keypoints. Various imputation techniques like mean imputation, interpolation, or predictive imputation can be utilized based on the nature of the missing data. Adjust the verification step in the preparation stage to account for missing values when calculating occurrences of patterns. This may involve considering neighboring data points or applying specific rules for handling missing values during pattern matching. Dealing with Irregular Sampling Intervals: Modify the keypoint extraction algorithm to handle irregular sampling intervals by considering the time gaps between data points. The algorithm should be able to identify significant changes in trends even with varying time intervals. Adapt the support calculation method in the superpattern mining phase to accommodate irregular sampling intervals. This may involve adjusting the way occurrences are counted and considering the time elapsed between data points when determining pattern matches. Enhancing Fusion Pattern Calculation: Enhance the fusion pattern calculation to handle missing data and irregular sampling intervals by incorporating flexibility in pattern matching. This could involve considering a range of values or time intervals for fusion pattern creation to capture patterns effectively despite data inconsistencies. Implement a mechanism to adjust the fusion pattern generation based on the availability and reliability of data points, ensuring that the algorithm can still identify meaningful patterns even in the presence of missing or irregular data. By incorporating these adaptations, the COP-Miner algorithm can be extended to effectively handle time series with missing data or irregular sampling intervals, enabling robust pattern mining in diverse data scenarios.

What other applications beyond time series prediction could benefit from the COP mining approach, and how would the algorithm need to be adapted

The COP mining approach can find applications beyond time series prediction in various domains where sequential pattern analysis is valuable. Some potential applications include: Healthcare Monitoring: COP mining can be applied to analyze patient health data over time, identifying patterns related to specific medical conditions or treatment outcomes. The algorithm may need adaptation to consider the unique characteristics of healthcare data and the importance of certain trends in patient monitoring. Supply Chain Management: In supply chain operations, COP mining can help detect patterns in inventory levels, demand fluctuations, or delivery schedules. Adapting the algorithm for supply chain data may involve incorporating factors like lead times, order quantities, and supplier performance metrics into pattern analysis. Fraud Detection: Utilizing COP mining for detecting fraudulent activities in financial transactions or online behaviors can be beneficial. The algorithm may require adjustments to handle the dynamic nature of fraud patterns and the need for real-time detection capabilities. Social Media Analysis: COP mining can be employed to uncover trends in social media interactions, sentiment analysis, or user engagement patterns. Adapting the algorithm for social media data may involve considering factors like user behavior, content relevance, and temporal dynamics in pattern discovery. Adapting the COP-Miner algorithm for these applications would involve customizing the pattern mining process to suit the specific data characteristics and objectives of each domain, ensuring accurate and meaningful pattern identification for actionable insights.

Can the keypoint extraction and fusion pattern concepts be applied to other types of sequential pattern mining problems beyond order-preserving patterns

The concepts of keypoint extraction and fusion patterns can be applied to various sequential pattern mining problems beyond order-preserving patterns. Some potential applications and adaptations include: Sequential Event Analysis: In event sequence analysis, keypoint extraction can help identify critical events or transitions in a sequence, while fusion patterns can capture combined event occurrences. Adapting the concepts for event sequences may involve considering event dependencies, temporal relationships, and event frequency in pattern mining. Text Mining and Natural Language Processing: Keypoint extraction can be utilized in text mining to identify significant changes or patterns in textual data, while fusion patterns can capture combined text patterns or phrases. Adapting the concepts for text analysis may involve considering word sequences, semantic relationships, and context-based patterns in text data. Image and Video Analysis: Keypoint extraction can play a role in identifying key features or changes in image and video data, while fusion patterns can capture combined visual patterns or object interactions. Adapting the concepts for image and video analysis may involve considering spatial relationships, object recognition, and temporal sequences in visual data patterns. By applying keypoint extraction and fusion pattern concepts to diverse sequential pattern mining problems, researchers and practitioners can enhance pattern discovery across various data types and domains, enabling more comprehensive and insightful analysis of sequential data.