论文标题
cratos：对时间序列最佳解决方案的可靠算法的认知
CRATOS: Cognition of Reliable Algorithm for Time-series Optimal Solution
论文作者
论文摘要
时间序列的异常检测在可靠性系统工程中起重要作用。但是，在实际应用中，在不同的应用方案中，正常行为和异常行为之间没有精确定义的边界。因此，在不同情况下，应采用不同的异常检测算法和过程。尽管这种策略提高了异常检测的准确性，但从业者将各种算法配置为数百万个系列，这大大增加了异常检测过程的开发和维护成本。在本文中，我们提出了Cratos，它是一种从时间序列中提取特征的自适应算法，然后将具有相似特征的群集序列列入一组。对于每个组，我们利用进化算法来搜索最佳的异常检测方法和过程。我们的方法可以大大降低开发和维持异常检测的成本。根据实验，我们的聚类方法实现了最先进的结果。本文中异常检测算法的准确性为85.1％。
Anomaly detection of time series plays an important role in reliability systems engineering. However, in practical application, there is no precisely defined boundary between normal and anomalous behaviors in different application scenarios. Therefore, different anomaly detection algorithms and processes ought to be adopted for time series in different situation. Although such strategy improve the accuracy of anomaly detection, it takes a lot of time for practitioners to configure various algorithms to millions of series, which greatly increases the development and maintenance cost of anomaly detection processes. In this paper, we propose CRATOS which is a self-adapt algorithms that extract features from time series, and then cluster series with similar features into one group. For each group we utilize evolutionary algorithm to search the best anomaly detection methods and processes. Our methods can significantly reduce the cost of development and maintenance of anomaly detection. According to experiments, our clustering methods achieves the state-of-art results. The accuracy of the anomaly detection algorithms in this paper is 85.1%.
