论文标题
MTS-Cyclegan：基于对抗性的深度映射学习网络，用于应用于熨斗行业的多元时间序列域适应
MTS-CycleGAN: An Adversarial-based Deep Mapping Learning Network for Multivariate Time Series Domain Adaptation Applied to the Ironmaking Industry
论文作者
论文摘要
在当前时代，为工业流程的自动化而生成了越来越多的机器学习模型。为此，使用每个单一资产的历史数据对机器学习模型进行培训，从而导致基于资产的模型的开发。为了将机器学习模型提升到更高水平的学习能力，域的适应性为从几个资产合并在一起的相关模式打开了大门。在这项研究中，我们着重于将基于资产的特定历史数据（源域）转化为与一个参考资产（目标域）相对应的数据，从而导致创建训练域不变的通用机器学习模型所需的多资产的全局数据集。这项研究是为了将域的适应性应用于熨斗行业，尤其是通过收集来自不同爆炸炉的数据来创建域不变数据集。高炉数据以多元时间序列为特征。文献中尚未广泛涵盖多元时间序列数据的域适应性。我们提出了MTS-Cyclegan，这是一种基于Cyclegan的多元时间序列数据的算法。据我们所知，这是第一次应用于多元时间序列数据。我们的贡献是在长期短期内存（LSTM）基于生成器的自动编码器（AE）的Cyclegan体系结构中的集成，以及基于LSTM的歧视器，以及专用的扩展功能提取机制。使用两个人工数据集对MTS-Cyclegan进行验证，该数据集嵌入了反映喷气炉过程的变量之间的复杂时间关系。 MTS-Cyclegan正在成功地学习两个人工多元时间序列数据集之间的映射，从而使从源到目标人工爆炸炉数据集有效地翻译。
In the current era, an increasing number of machine learning models is generated for the automation of industrial processes. To that end, machine learning models are trained using historical data of each single asset leading to the development of asset-based models. To elevate machine learning models to a higher level of learning capability, domain adaptation has opened the door for extracting relevant patterns from several assets combined together. In this research we are focusing on translating the specific asset-based historical data (source domain) into data corresponding to one reference asset (target domain), leading to the creation of a multi-assets global dataset required for training domain invariant generic machine learning models. This research is conducted to apply domain adaptation to the ironmaking industry, and particularly for the creation of a domain invariant dataset by gathering data from different blast furnaces. The blast furnace data is characterized by multivariate time series. Domain adaptation for multivariate time series data hasn't been covered extensively in the literature. We propose MTS-CycleGAN, an algorithm for Multivariate Time Series data based on CycleGAN. To the best of our knowledge, this is the first time CycleGAN is applied on multivariate time series data. Our contribution is the integration in the CycleGAN architecture of a Long Short-Term Memory (LSTM)-based AutoEncoder (AE) for the generator and a stacked LSTM-based discriminator, together with dedicated extended features extraction mechanisms. MTS-CycleGAN is validated using two artificial datasets embedding the complex temporal relations between variables reflecting the blast furnace process. MTS-CycleGAN is successfully learning the mapping between both artificial multivariate time series datasets, allowing an efficient translation from a source to a target artificial blast furnace dataset.
