Discovering long term dependencies in noisy time series data using deep learning
This work tackles the problem of interpretability in time series modeling for manufacturing engineers, but appears incremental as it applies existing deep learning methods to this domain.
The paper developed a framework for capturing and explaining temporal dependencies in time series data using deep neural networks, testing it on synthetic and real-world datasets to address interpretability needs in manufacturing processes.
Time series modelling is essential for solving tasks such as predictive maintenance, quality control and optimisation. Deep learning is widely used for solving such problems. When managing complex manufacturing process with neural networks, engineers need to know why machine learning model made specific decision and what are possible outcomes of following model recommendation. In this paper we develop framework for capturing and explaining temporal dependencies in time series data using deep neural networks and test it on various synthetic and real world datasets.