Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Switching State Space Model (DS$^3$M) for Nonlinear Time Series
  Forecasting with Regime Switching   We propose a deep switching state space model (DS$^3$M) for efficient
inference and forecasting of nonlinear time series with irregularly switching
among various regimes. The switching among regimes is captured by both discrete
and continuous latent variables with recurrent neural networks. The model is
estimated with variational inference using a reparameterization trick. We test
the approach on a variety of simulated and real datasets. In all cases, DS$^3$M
achieves competitive performance compared to several state-of-the-art methods
(e.g. GRU, SRNN, DSARF, SNLDS), with superior forecasting accuracy, convincing
interpretability of the discrete latent variables, and powerful representation
of the continuous latent variables for different kinds of time series.
Specifically, the MAPE values increase by 0.09\% to 15.71\% against the
second-best performing alternative models.

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The categories of this paper are: cmp-lg
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The categories of this paper are: econ
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The categories of this paper are: astro-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: quant-ph
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The categories of this paper are: q-bio
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The categories of this paper are: nlin
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The categories of this paper are: acc-phys
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The categories of this paper are: funct-an
0
The categories of this paper are: hep-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: chao-dyn
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The categories of this paper are: solv-int
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The categories of this paper are: math
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The categories of this paper are: bayes-an
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The categories of this paper are: dg-ga