论文标题
时间序列中的采矿季节性时间模式
Mining Seasonal Temporal Patterns in Time Series
论文作者
论文摘要
越来越大的时间序列可以从更广泛的启用IoT传感器中获得，可以通过从中挖掘时间模式从中获得重要的见解。在许多实际应用中发现的一种有用的模式类型表现出周期性的发生，因此被称为季节性时间模式（STP）。与常规模式相比，采矿季节性时间模式更具挑战性，因为传统措施（例如支持和信心）不会捕获季节性特征。此外，抗单调性属性不适合STP，因此导致了指数搜索空间。本文介绍了我们从时间序列（FREQSTPFTS）解决方案中频繁的季节性时间模式挖掘提供：（1）时间序列的第一个季节性时间图案挖掘（STPM）的解决方案，可以在不同的数据粒度下开采STP。 （2）使用有效的数据结构和两种修剪技术来减少搜索空间并加快采矿过程的STPM算法。 （3）使用共同信息（数据相关性的度量）从搜索空间中修剪无主张的时间序列的近似STPM。 （4）广泛的实验评估表明，STPM在运行时和内存消耗中的基线表现优于基线，并且可以扩展到大数据集。与基线相比，近似STPM的数量级要快，并且存储器消耗较少，同时保持高精度。
Very large time series are increasingly available from an ever wider range of IoT-enabled sensors, from which significant insights can be obtained through mining temporal patterns from them. A useful type of patterns found in many real-world applications exhibits periodic occurrences, and is thus called seasonal temporal pattern (STP). Compared to regular patterns, mining seasonal temporal patterns is more challenging since traditional measures such as support and confidence do not capture the seasonality characteristics. Further, the anti-monotonicity property does not hold for STPs, and thus, resulting in an exponential search space. This paper presents our Frequent Seasonal Temporal Pattern Mining from Time Series (FreqSTPfTS) solution providing: (1) The first solution for seasonal temporal pattern mining (STPM) from time series that can mine STP at different data granularities. (2) The STPM algorithm that uses efficient data structures and two pruning techniques to reduce the search space and speed up the mining process. (3) An approximate version of STPM that uses mutual information, a measure of data correlation, to prune unpromising time series from the search space. (4) An extensive experimental evaluation showing that STPM outperforms the baseline in runtime and memory consumption, and can scale to big datasets. The approximate STPM is up to an order of magnitude faster and less memory consuming than the baseline, while maintaining high accuracy.
