MTS-CycleGAN: An Adversarial-based Deep Mapping Learning Network for Multivariate Time Series Domain Adaptation Applied to the Ironmaking Industry

TL;DR

This paper introduces MTS-CycleGAN, a novel deep learning model that applies CycleGAN to multivariate time series data for domain adaptation in the ironmaking industry, enabling effective data translation between blast furnaces.

Contribution

It is the first application of CycleGAN on multivariate time series data, integrating LSTM-based autoencoders and discriminators for improved domain adaptation.

Findings

Successfully learned mappings between artificial multivariate time series datasets

Demonstrated effective translation from source to target blast furnace data

Validated on complex artificial datasets reflecting blast furnace processes

Abstract

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.