no code implementations • 5 Aug 2020 • Cedric Schockaert
An advanced conceptual validation framework for multimodal multivariate time series defines a multi-level contextual anomaly detection ranging from an univariate context definition, to a multimodal abstract context representation learnt by an Autoencoder from heterogeneous data (images, time series, sounds, etc.)
no code implementations • 15 Jul 2020 • Cedric Schockaert, Reinhard Leperlier, Assaad Moawad
In the research presented in this paper, we focus on the development of an interpretable multivariate time series forecasting deep learning architecture for the temperature of the hot metal produced by a blast furnace.
no code implementations • 15 Jul 2020 • Cedric Schockaert, Henri Hoyez
The blast furnace data is characterized by multivariate time series.
no code implementations • 15 Jul 2020 • Cedric Schockaert, Vadim Macher, Alexander Schmitz
In comparison with LIME, VAE-LIME is showing a significantly improved local fidelity of the local interpretable linear model with the black-box model resulting in robust model interpretability.