Time series classification (TSC) gained a lot of attention in the pastdecade and number of methods for representing and classifying time series havebeen proposed. Nowadays, methods based on convolutional networks and ensembletechniques represent the state of the art for time series classification. Techniquestransforming time series to image or text also provide reliable ways to extractmeaningful features or representations of time series. We compare the state-of-the-art representation and classification methods on a specific application, thatis predictive maintenance from sequences of event logs. The contributions of thispaper are twofold: introducing a new data set for predictive maintenance on auto-mated teller machines (ATMs) log data and comparing the performance of differentrepresentation methods for predicting the occurrence of a breakdown. The prob-lem is difficult since unlike the classic case of predictive maintenance via signalsfrom sensors, we have sequences of discrete event logs occurring at any time andthe lengths of the sequences, corresponding to life cycles, vary a lot.

翻译：在过去的十年中,时间序列分类(TSC)在过去十年中引起了很大的注意,代表时间序列和分类方法的数量也引起了人们的注意。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。技术将时间序列转换成图像或文本也提供了可靠的方法来提取时间序列的明显特征或表示。我们比较了特定应用中的最新代表性和分类方法,这是对事件日志序列的预测性维护。本文的贡献具有双重性:引入了一套新的数据集,用于自动成形计时机日志数据的预测性维护,并比较了预测发生故障的不同代表方法的性能。由于与通过传感器信号进行预测性维护的典型案例不同,我们拥有随时发生的离散事件日志序列和序列长度的顺序,与寿命周期相对应,变化很大。