Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Explainable nonlinear modelling of multiple time series with invertible
  neural networks   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.

17
The categories of this paper are: q-fin
35
The categories of this paper are: mtrl-th
25
The categories of this paper are: patt-sol
32
The categories of this paper are: atom-ph
31
The categories of this paper are: ao-sci
23
The categories of this paper are: solv-int
13
The categories of this paper are: quant-ph
14
The categories of this paper are: math-ph
16
The categories of this paper are: stat
37
The categories of this paper are: supr-con
27
The categories of this paper are: comp-gas
24
The categories of this paper are: dg-ga
3
The categories of this paper are: physics
29
The categories of this paper are: funct-an
12
The categories of this paper are: nucl-th
10
The categories of this paper are: q-bio