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.

翻译：来自不同领域的时间序列数据持续增长，从这些序列中提取和分析时序模式能够提供有价值的洞见。时序模式挖掘（TPM）通过将事件时间间隔纳入模式，扩展了传统模式挖掘，使其表达能力更强，但也增加了时间和空间上的计算复杂度。稀有时序模式（RTP）是一种重要的时序模式类型，其出现频率低但置信度高。挖掘这些稀有模式面临若干挑战，例如，低支持度阈值可能导致组合爆炸并产生大量无关模式。为解决此问题，一种高效的稀有时序模式挖掘方法至关重要。本文提出了从时间序列中挖掘稀有时序模式（RTPMfTS）的方法，旨在发现稀有时序模式。本工作的主要贡献如下：（1）一个端到端的RTPMfTS流程，以时间序列数据作为输入并输出稀有时序模式。（2）一种高效的稀有时序模式挖掘（RTPM）算法，该算法利用优化的数据结构实现快速的事件和模式检索，并采用有效的剪枝技术以加速挖掘过程。（3）对RTPM进行了全面的实验评估，证明其在运行时间和内存效率上均优于基线方法。