Zhao Qiang, Li Qing, Yu Deshui, Han Yinghua
School of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China.
College of Computer and Communication Engineering, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China.
Entropy (Basel). 2021 Mar 19;23(3):365. doi: 10.3390/e23030365.
In many industrial domains, there is a significant interest in obtaining temporal relationships among multiple variables in time-series data, given that such relationships play an auxiliary role in decision making. However, when transactions occur frequently only for a period of time, it is difficult for a traditional time-series association rules mining algorithm (TSARM) to identify this kind of relationship. In this paper, we propose a new TSARM framework and a novel algorithm named TSARM-UDP. A TSARM mining framework is used to mine time-series association rules (TSARs) and an up-to-date pattern (UDP) is applied to discover rare patterns that only appear in a period of time. Based on the up-to-date pattern mining, the proposed TSAR-UDP method could extract temporal relationship rules with better generality. The rules can be widely used in the process industry, the stock market, etc. Experiments are then performed on the public stock data and real blast furnace data to verify the effectiveness of the proposed algorithm. We compare our algorithm with three state-of-the-art algorithms, and the experimental results show that our algorithm can provide greater efficiency and interpretability in TSARs and that it has good prospects.
在许多工业领域，人们对获取时间序列数据中多个变量之间的时间关系有着浓厚兴趣，因为这种关系在决策中起着辅助作用。然而，当交易仅在一段时间内频繁发生时，传统的时间序列关联规则挖掘算法（TSARM）很难识别这种关系。在本文中，我们提出了一种新的TSARM框架和一种名为TSARM-UDP的新颖算法。一个TSARM挖掘框架用于挖掘时间序列关联规则（TSAR），并应用一种最新模式（UDP）来发现仅在一段时间内出现的罕见模式。基于最新模式挖掘，所提出的TSAR-UDP方法可以提取具有更好通用性的时间关系规则。这些规则可广泛应用于过程工业、股票市场等领域。然后，我们对公开股票数据和实际高炉数据进行实验，以验证所提算法的有效性。我们将我们的算法与三种最先进的算法进行比较，实验结果表明，我们的算法在TSAR方面能够提供更高的效率和可解释性，并且具有良好的前景。
