The Arc Loss Dataset

The Arc Loss Dataset is a benchmark dataset designed for time series classification in industrial applications, particularly for fault detection and diagnosis (FDD). It was collected from a large-scale pyrometallurgical plant and captures the real-world complexities of industrial processes, including high dimensionality, sensor noise, and variable system dynamics. The dataset consists of 3,226 multivariate time series samples, each containing 1,101 time steps (equivalent to 55 minutes) with 96 process variables. The dataset is split into three subsets: I) train.pt (70% of the samples, 2,258 samples), II) val.pt (10%, 323 samples), and III) test.pt (20%, 645 samples). More information can be found in the README.txt弧损失数据集（Arc Loss Dataset）是一款专为工业场景下时间序列分类任务打造的基准数据集，尤其适用于故障检测与诊断（Fault Detection and Diagnosis，简称FDD）。该数据集采集自一座大型火法冶金工厂，完整捕捉了工业生产流程的真实复杂特性，涵盖高维度特征、传感器噪声以及可变的系统动力学特性。数据集共包含3226个多变量时间序列样本，每个样本涵盖1101个时间步（对应时长55分钟），涉及96个过程变量。数据集被划分为三个子集：I) train.pt（占总样本的70%，共计2258个样本），II) val.pt（占10%，共计323个样本），III) test.pt（占20%，共计645个样本）。更多相关信息可查阅README.txt。