Fetching the paper…
Reading the bibliography…
We introduce Gluon Time Series (GluonTS, available at https://gluon-ts.mxnet.io), a library for deep-learning-based time series modeling.
An analysis of transformations
G. E. P. Box and D. R. Cox · 1964
Earlier work this paper cites.
Forecasting with artificial neural networks:: The state of the art
Guoqiang Zhang, B Eddy Patuwo, and Michael Y Hu · 1998
Earlier work this paper cites.
On discriminative vs. generative classifiers: A comparison of logistic regression and naive Bayes
Andrew Y. Ng and Michael I. Jordan · 2002
Earlier work this paper cites.
Gaussian process priors with uncertain inputs application to multiple-step ahead time series forecasting
Agathe Girard, Carl Edward Rasmussen, Joaquin Quinonero Candela, and Roderick Murray-Smith · 2003
Earlier work this paper cites.
Quantile Regression
Roger Koenker · 2005
Earlier work this paper cites.
Matplotlib: A 2D graphics environment
J. D. Hunter · 2007
Earlier work this paper cites.
Forecasting with Exponential Smoothing: The State Space Approach
R. Hyndman, A. B. Koehler, J. K. Ord, and R. D. Snyder · 2008
Earlier work this paper cites.
Automatic time series forecasting: the forecast package for R
Rob J Hyndman and Yeasmin Khandakar · 2008
Earlier work this paper cites.
Scikit-learn: Machine learning in Python
Fabian Pedregosa et al · 2011
Earlier work this paper cites.
Time series analysis by state space methods , volume 38
James Durbin and Siem Jan Koopman · 2012
Earlier work this paper cites.
Effective Bayesian modeling of groups of related count time series
Nicolas Chapados · 2014
Earlier work this paper cites.
Predicting the present with Bayesian structural time series
Steven L. Scott and Hal R. Varian · 2014
Earlier work this paper cites.
Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le · 2014
Earlier work this paper cites.
Mxnet: A flexible and efficient machine learning library for heterogeneous distributed systems
Tianqi Chen et al · 2015
Earlier work this paper cites.
Mllib: Machine learning in apache spark
Xiangrui Meng et al · 2015
Earlier work this paper cites.
Variational inference with normalizing flows
Danilo Jimenez Rezende and Shakir Mohamed · 2015
Cited alongside, same era.
Tensorflow: A system for large-scale machine learning
Martín Abadi et al · 2016
Cited alongside, same era.
Sequential neural models with stochastic layers
Marco Fraccaro, Søren Kaae Sønderby, Ulrich Paquet, and Ole Winther · 2016
Cited alongside, same era.
Non-parametric time series forecaster
Jan Gasthaus · 2016
Cited alongside, same era.
Bayesian intermittent demand forecasting for large inventories
Matthias W Seeger, David Salinas, and Valentin Flunkert · 2016
Cited alongside, same era.
WaveNet: A generative model for raw audio
Aäron van den Oord et al · 2016
Cited alongside, same era.
Dominique T Shipmon, Jason M Gurevitch, Paolo M Piselli, and Steve Edwards · 2017
Later among the works it cites.
Forecasting at scale
Sean J Taylor and Benjamin Letham · 2017
Later among the works it cites.
Attention is all you need
Ashish Vaswani et al · 2017
Later among the works it cites.
A multi-horizon quantile recurrent forecaster
Ruofeng Wen Wen, Kari Torkkola, and Balakrishnan Narayanaswamy · 2017
Later among the works it cites.
”Deep” learning for missing value imputation in tables with non-numerical data
Felix Biessmann, David Salinas, Sebastian Schelter, Philipp Schmidt, and Dustin Lange · 2018
Later among the works it cites.
Pyro: Deep Universal Probabilistic Programming
Eli Bingham et al · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Quantile autoregression neural network model with applications to evaluating value at risk
Qifa Xu, Xi Liu, Cuixia Jiang, and Keming Yu · 2016
Cited alongside, same era.
Probabilistic demand forecasting at scale
Joos-Hendrik Böse et al · 2017
Cited alongside, same era.
UCI machine learning repository, 2017
Dua Dheeru and Efi Karra Taniskidou · 2017
Cited alongside, same era.
Joshua V. Dillon et al · 2017
Cited alongside, same era.
Forecasting: Principles and practice
Rob J Hyndman and George Athanasopoulos · 2017
Cited alongside, same era.
Structured inference networks for nonlinear state space models
Rahul G Krishnan, Uri Shalit, and David Sontag · 2017
Cited alongside, same era.
MXFusion: A modular deep probabilistic programming library
Zhenwen Dai, Eric Meissner, and Neil D. Lawrence · 2018
Later among the works it cites.
Forecasting big time series: old and new
Christos Faloutsos, Jan Gasthaus, Tim Januschowski, and Yuyang Wang · 2018
Later among the works it cites.
The Sockeye neural machine translation toolkit at AMTA 2018
Felix Hieber et al · 2018
Later among the works it cites.
Deep factors with Gaussian processes for forecasting
Danielle C Maddix, Yuyang Wang, and Alex Smola · 2018
Later among the works it cites.
The M4 competition: Results, findings, conclusion and way forward
Spyros Makridakis, Evangelos Spiliotis, and Vassilios Assimakopoulos · 2018
Later among the works it cites.
Deep state space models for time series forecasting
Syama Sundar Rangapuram, Matthias Seeger, Jan Gasthaus, Lorenzo Stella, Yuyang Wang, and Tim Januschowski · 2018
Later among the works it cites.
M4 forecasting competition: Introducing a new hybrid ES-RNN model
Slawek Smyl, Jai Ranganathan, and Andrea Pasqua · 2018
Later among the works it cites.
Probabilistic forecasting with Spline Quantile Function RNNs
Jan Gasthaus, Konstantinos Benidis, Yuyang Wang, Syama Sundar Rangapuram, David Salinas, Valentin Flunkert, and Tim Januschowski · 2019
Closest in time.
Deep factors for forecasting
Yuyang Wang, Alex Smola, Danielle Maddix, Jan Gasthaus, Dean Foster, and Tim Januschowski · 2019
Closest in time.