Time series classification (TSC) gained a lot of attention in the past decade and number of methods for representing and classifying time series have been proposed. Nowadays, methods based on convolutional networks and ensemble techniques represent the state of the art for time series classification. Techniques transforming time series to image or text also provide reliable ways to extract meaningful features or representations of time series. We compare the state-of-the-art representation and classification methods on a specific application, that is predictive maintenance from sequences of event logs. The contributions of this paper are twofold: introducing a new data set for predictive maintenance on automated teller machines (ATMs) log data and comparing the performance of different representation methods for predicting the occurrence of a breakdown. The problem is difficult since unlike the classic case of predictive maintenance via signals from sensors, we have sequences of discrete event logs occurring at any time and the lengths of the sequences, corresponding to life cycles, vary a lot.

翻译：在过去十年中,时间序列分类(TSC)引起了许多注意,并提出了代表时间序列和分类方法的数目。如今,基于革命网络和混合技术的方法代表了时间序列分类的先进程度。将时间序列转换成图像或文字的技术也提供了可靠的方法来提取有意义的特征或时间序列的表示方式。我们比较了具体应用中的最新代表性和分类方法,即根据事件日志的序列进行预测性维护。本文的贡献有两个方面:引进一套新的数据集,用于自动出纳机日志数据的预测性维护,并比较预测发生故障的不同代表方法的性能。这个问题很困难,因为与通过传感器信号进行预测性维护的典型案例不同,我们有随时发生的离散事件日志的序列和序列的长度,与寿命周期相对应,差异很大。