Efficient Rare Temporal Pattern Mining in Time Series

TL;DR

This paper presents RTPMfTS, an efficient method for mining rare temporal patterns in time series data, addressing computational challenges with optimized algorithms and demonstrating superior performance over baselines.

Contribution

Introduction of RTPMfTS, an end-to-end framework with a novel RTPM algorithm that improves speed and memory efficiency in rare temporal pattern mining.

Findings

RTPMfTS outperforms baseline methods in runtime.

RTPMfTS uses optimized data structures for fast pattern retrieval.

Experimental results show significant memory savings.

Abstract

Time series data from various domains is continuously growing, and extracting and analyzing temporal patterns within these series can provide valuable insights. Temporal pattern mining (TPM) extends traditional pattern mining by incorporating event time intervals into patterns, making them more expressive but also increasing the computational complexity in terms of time and space. One important type of temporal pattern is the rare temporal pattern (RTP), which occurs infrequently but with high confidence. Mining these rare patterns poses several challenges, for example, the low support threshold can lead to a combinatorial explosion and the generation of many irrelevant patterns. To address this, an efficient approach to mine rare temporal patterns is essential. This paper introduces the Rare Temporal Pattern Mining from Time Series (RTPMfTS) method, designed to discover rare temporal patterns. The key contributions of this work are as follows: (1) An end-to-end RTPMfTS process that takes time series data as input and outputs rare temporal patterns. (2) A highly efficient Rare Temporal Pattern Mining (RTPM) algorithm, which leverages optimized data structures for fast event and pattern retrieval, as well as effective pruning techniques to accelerate the mining process. (3) A comprehensive experimental evaluation of RTPM, demonstrating that it outperforms the baseline in both runtime and memory efficiency.