Efficient Rare Temporal Pattern Mining in Time Series

Abstract

Time series data from various domains are increasing continuously. Extracting and analyzing the temporal patterns in these series can reveal significant insights. Temporal pattern mining (TPM) extends traditional pattern mining by incorporating event time intervals into extracted patterns, enhancing their expressiveness but increasing time and space complexities. One valuable type of temporal pattern is known as rare temporal patterns (RTPs), which occur rarely but with high confidence. There exist several challenges when mining rare temporal patterns. The support measure is set very low, leading to a further combinatorial explosion and potentially producing too many uninteresting patterns. Thus, an efficient approach to rare temporal pattern mining is needed. This paper introduces our Rare Temporal Pattern Mining from Time Series (RTPMfTS) method for discovering rare temporal patterns, featuring the following key contributions: (1) An end-to-end RTPMfTS process that takes time series data as input and yields rare temporal patterns as output. (2) An efficient Rare Temporal Pattern Mining (RTPM) algorithm that uses optimized data structures for quick event and pattern retrieval and utilizes effective pruning techniques for much faster mining. (3) A thorough experimental evaluation of RTPM, showing that RTPM outperforms the baseline in terms of runtime and memory usage.