\begin{thebibliography}{50}
\providecommand{\natexlab}[1]{#1}
\providecommand{\url}[1]{\texttt{#1}}
\expandafter\ifx\csname urlstyle\endcsname\relax
  \providecommand{\doi}[1]{doi: #1}\else
  \providecommand{\doi}{doi: \begingroup \urlstyle{rm}\Url}\fi

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Ahmed Alaa and Mihaela van~der Schaar.
\newblock Attentive state-space modeling of disease progression.
\newblock In \emph{NeurIPS}, 2019.

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Tayfun Alpay, Stefan Heinrich, and Stefan Wermter.
\newblock Learning multiple timescales in recurrent neural networks.
\newblock In \emph{ICANN}, 2016.

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Axel Brando, Jos{\'{e}}~A. Rodr{\'{\i}}guez{-}Serrano, Mauricio Ciprian,
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\newblock Uncertainty modelling in deep networks: Forecasting short and noisy
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\newblock In \emph{ECML-PKDD}, 2018.

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  Richard~E. Turner.
\newblock Scalable exact inference in multi-output gaussian processes.
\newblock In \emph{ICML}, 2020.

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Kyunghyun Cho, Bart van Merrienboer, Dzmitry Bahdanau, and Yoshua Bengio.
\newblock On the properties of neural machine translation: Encoder-decoder
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\newblock In \emph{Workshop on Syntax, Semantics and Structure in Statistical
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Junyoung Chung, Caglar Gulcehre, Kyunghyun Cho, and Yoshua Bengio.
\newblock Empirical evaluation of gated recurrent neural networks on sequence
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\newblock In \emph{NIPS Workshop on Deep Learning}, 2014.

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Giorgio Corani, Alessio Benavoli, and Marco Zaffalon.
\newblock Time series forecasting with gaussian processes needs priors.
\newblock In \emph{ECML-PKDD}, 2021.

\bibitem[Dingli and Fournier(2017)]{dingli2017financial}
Alexiei Dingli and Karl~Sant Fournier.
\newblock Financial time series forecasting--a deep learning approach.
\newblock \emph{International Journal of Machine Learning and Computing}, 2017.

\bibitem[Flores(1986)]{FLORES198693}
Benito~E Flores.
\newblock A pragmatic view of accuracy measurement in forecasting.
\newblock \emph{Omega}, 1986.

\bibitem[Gal and Ghahramani(2016)]{GalG16}
Yarin Gal and Zoubin Ghahramani.
\newblock A theoretically grounded application of dropout in recurrent neural
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\newblock In \emph{NeurIPS}, 2016.

\bibitem[Gneiting and Raftery(2007)]{gneiting2007strictly}
Tilmann Gneiting and Adrian~E Raftery.
\newblock Strictly proper scoring rules, prediction, and estimation.
\newblock \emph{Journal of the American statistical Association}, 2007.

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Eric~P Grimit, Tilmann Gneiting, Veronica~J Berrocal, and Nicholas~A Johnson.
\newblock The continuous ranked probability score for circular variables and
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\newblock \emph{Quarterly Journal of the Royal Meteorological Society}, 2006.

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Chuan Guo, Geoff Pleiss, Yu~Sun, and Kilian~Q Weinberger.
\newblock On calibration of modern neural networks.
\newblock In \emph{ICML}, 2017.

\bibitem[He et~al.(2016)He, Zhang, Ren, and Sun]{he2016deep}
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun.
\newblock Deep residual learning for image recognition.
\newblock In \emph{CVPR}, 2016.

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Sepp Hochreiter and J{\"u}rgen Schmidhuber.
\newblock Long short-term memory.
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\newblock Online structure learning for feed-forward and recurrent sum-product
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\newblock In \emph{NeurIPS}, 2018.

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\newblock Enhancing the locality and breaking the memory bottleneck of
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\newblock In \emph{NeurIPS}, 2019.

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Faming Liang.
\newblock Bayesian neural networks for nonlinear time series forecasting.
\newblock \emph{Statistics and Computing}, 2005.

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Spyros Makridakis.
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\newblock The m4 competition: 100,000 time series and 61 forecasting methods.
\newblock \emph{International Journal of Forecasting}, 2020.

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\newblock Dynamic sum product networks for tractable inference on sequence
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\bibitem[Nickisch and Rasmussen(2008)]{nickisch2008approximations}
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\newblock Approximations for binary gaussian process classification.
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\newblock In \emph{ICLR}, 2019.

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\newblock Einsum networks: Fast and scalable learning of tractable
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\newblock In \emph{ICML}, 2020.

\bibitem[Poon and Domingos(2011)]{poon2011sum}
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\newblock Sum-product networks: a new deep architecture.
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\newblock Deep state space models for time series forecasting.
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\newblock Multivariate probabilistic time series forecasting via conditioned
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\newblock In \emph{ICLR}, 2021{\natexlab{b}}.

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\newblock \emph{International Journal of Forecasting}, 2020.

\bibitem[Seeger(2004)]{seeger2004gaussian}
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\newblock Conformal time-series forecasting.
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Zhongjie Yu, Mingye Zhu, Martin Trapp, Arseny Skryagin, and Kristian Kersting.
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\end{thebibliography}
