Big time series are increasingly available from an ever wider range of IoT-enabled sensors deployed in various environments. Significant insights can be gained by mining temporal patterns from these time series. Temporal pattern mining (TPM) extends traditional pattern mining by adding event time intervals into extracted patterns, making them more expressive at the expense of increased time and space complexities. Besides frequent temporal patterns (FTPs), which occur frequently in the entire dataset, another useful type of temporal patterns are so-called rare temporal patterns (RTPs), which appear rarely but with high confidence. Mining rare temporal patterns yields additional challenges. For FTP mining, the temporal information and complex relations between events already create an exponential search space. For RTP mining, the support measure is set very low, leading to a further combinatorial explosion and potentially producing too many uninteresting patterns. Thus, there is a need for a generalized approach which can mine both frequent and rare temporal patterns. This paper presents our Generalized Temporal Pattern Mining from Time Series (GTPMfTS) approach with the following specific contributions: (1) The end-to-end GTPMfTS process taking time series as input and producing frequent/rare temporal patterns as output. (2) The efficient Generalized Temporal Pattern Mining (GTPM) algorithm mines frequent and rare temporal patterns using efficient data structures for fast retrieval of events and patterns during the mining process, and employs effective pruning techniques for significantly faster mining. (3) An approximate version of GTPM that uses mutual information, a measure of data correlation, to prune unpromising time series from the search space.

翻译：随着各类环境部署的物联网传感设备日益普及，大时间序列数据正以前所未有的规模涌现。从这些时间序列中挖掘时序模式可获取重要洞见。时序模式挖掘（TPM）通过为提取的模式添加事件时间间隔，在增加时间和空间复杂度的同时提升了模式表达能力。除了在整个数据集中高频出现的频繁时序模式（FTP）外，另一类具有实用价值的模式是所谓稀有时序模式（RTP），其出现频率低但置信度高。挖掘稀有时序模式面临更大挑战：对于FTP挖掘，事件间的时间信息与复杂关系已构成指数级搜索空间；而RTP挖掘中支持度阈值设定极低，将进一步引发组合爆炸，可能产生过多无趣模式。因此，亟需能够同时挖掘频繁与稀有时序模式的通用方法。本文提出面向时间序列的广义时序模式挖掘（GTPMfTS）方法，具体贡献如下：（1）构建端到端GTPMfTS流程，以时间序列为输入，输出频繁/稀有时序模式；（2）提出高效广义时序模式挖掘（GTPM）算法，通过高效数据结构实现挖掘过程中事件与模式的快速检索，并采用有效剪枝技术显著加速挖掘进程；（3）提出GTPM的近似版本，利用数据相关性度量——互信息，从搜索空间中剪除无价值的时间序列。