Explainable nonlinear modelling of multiple time series with invertible neural networks
This work addresses the need for explainable nonlinear models in time series analysis, though it appears incremental as it builds on linear VAR processes with neural network extensions.
The paper tackles the problem of nonlinear modeling of multiple time series by proposing a method based on invertible neural networks, assuming a latent vector autoregressive process and invertible observation mappings, with preliminary tests showing reduced prediction error.
A method for nonlinear topology identification is proposed, based on the assumption that a collection of time series are generated in two steps: i) a vector autoregressive process in a latent space, and ii) a nonlinear, component-wise, monotonically increasing observation mapping. The latter mappings are assumed invertible, and are modelled as shallow neural networks, so that their inverse can be numerically evaluated, and their parameters can be learned using a technique inspired in deep learning. Due to the function inversion, the back-propagation step is not straightforward, and this paper explains the steps needed to calculate the gradients applying implicit differentiation. Whereas the model explainability is the same as that for linear VAR processes, preliminary numerical tests show that the prediction error becomes smaller.