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Recent progress in neural forecasting accelerated improvements in the performance of large-scale forecasting systems.
Generating Long Sequences with Sparse Transformers
Child, R.; Gray, S.; Radford, A.; and Sutskever, I. 2019 · 1904
Earlier work this paper cites.
Best Linear Unbiased Interpolation, Distribution, and Extrapolation of Time Series by Related Series
Chow, G. C.; and loh Lin, A. 1971 · 1971
Earlier work this paper cites.
A Methodological Note on the Estimation of Time Series
Fernandez, R. B. 1981 · 1981
Earlier work this paper cites.
Approximation capabilities of multilayer feedforward networks
Hornik, K. 1991 · 1991
Earlier work this paper cites.
Ten lectures on wavelets
Daubechies, I. 1992 · 1992
Earlier work this paper cites.
Universal approximation bounds for superpositions of a sigmoidal function
Barron, A. R. 1993 · 1993
Earlier work this paper cites.
A chronology of interpolation: from ancient astronomy to modern signal and image processing
Meijering, E. 2002 · 2002
Earlier work this paper cites.
Global early warning systems for natural hazards: Systematic and people-centred
Basher, R. 2006 · 2006
Earlier work this paper cites.
A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series
Marcellino, M.; Stock, J. H.; and Watson, M. W. 2006 · 2006
Earlier work this paper cites.
MIDAS Regressions: Further Results and New Directions
Ghysels, E.; Sinko, A.; and Valkanov, R. 2007 · 2007
Earlier work this paper cites.
Automatic Time Series Forecasting: The forecast Package for R
Hyndman, R. J.; and Khandakar, Y. 2008 · 2008
Earlier work this paper cites.
Density forecasting for long-term peak electricity demand
Hyndman, R. J.; and Fan, S. 2009 · 2009
Earlier work this paper cites.
Forecasting with Mixed Frequencies
Armesto, M. T.; Engemann, K. M.; and Owyang, M. T. 2010 · 2010
Earlier work this paper cites.
Algorithms for Hyper-Parameter Optimization
Bergstra, J.; Bardenet, R.; Bengio, Y.; and Kégl, B. 2011 · 2011
Earlier work this paper cites.
Unsupervised Feature Learning and Deep Learning: A Review and New Perspectives
Bengio, Y.; Courville, A. C.; and Vincent, P. 2012 · 2012
Earlier work this paper cites.
Managing the risks of extreme events and disasters to advance climate change adaptation: special report of the intergovernmental panel on climate change
Field, C. B.; Barros, V.; Stocker, T. F.; and Dahe, Q. 2012 · 2012
Earlier work this paper cites.
Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting
Zhou, H.; Zhang, S.; Peng, J.; Zhang, S.; Li, J.; Xiong, H.; and Zhang, W. 2020 · 2012
Earlier work this paper cites.
Multi-step-ahead time series prediction using multiple-output support vector regression
Bao, Y.; Xiong, T.; and Hu, Z. 2014 · 2014
Cited alongside, same era.
A first course in wavelets with Fourier analysis
Boggess, A.; and Narcowich, F. J. 2015 · 2015
Cited alongside, same era.
ADAM: A Method for Stochastic Optimization
Kingma, D. P.; and Ba, J. 2014 · 2015
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A Bias and Variance Analysis for Multistep-Ahead Time Series Forecasting
Atiya, A.; and Taieb, B. 2016 · 2016
Cited alongside, same era.
The value of vital sign trends for detecting clinical deterioration on the wards
Churpek, M. M.; Adhikari, R.; and Edelson, D. P. 2016 · 2016
Cited alongside, same era.
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Chang, S.; Zhang, Y.; Han, W.; Yu, M.; Guo, X.; Tan, W.; Cui, X.; Witbrock, M.; Hasegawa-Johnson, M. A.; and Huang, T. S. 2017 · 2017
Latent ODEs for Irregularly-Sampled Time Series
Rubanova, Y.; Chen, R. T. Q.; and Duvenaud, D. 2019 · 2019
Later among the works it cites.
Interpolation-Prediction Networks for Irregularly Sampled Time Series
Shukla, S. N.; and Marlin, B. M. 2019 · 2019
Later among the works it cites.
An Optimized Heterogeneous Structure LSTM Network for Electricity Price Forecasting
Zhou, S.; Zhou, L.; Mao, M.; Tai, H.; and Wan, Y. 2019 · 2019
Later among the works it cites.
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Benidis, K.; Rangapuram, S. S.; Flunkert, V.; Wang, B.; Maddix, D.; Turkmen, C.; Gasthaus, J.; Bohlke-Schneider, M.; Salinas, D.; Stella, L.; Callot, L.; and Januschowski, T. 2020 · 2020
Later among the works it cites.
Reformer: The Efficient Transformer
Kitaev, N.; Łukasz Kaiser; and Levskaya, A. 2020 · 2020
Later among the works it cites.
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Approximating Continuous Functions by ReLU Nets of Minimal Width
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Time-series extreme event forecasting with neural networks at UBER
Laptev, N.; Yosinsk, J.; Erran, L. L.; and Smyl, S. 2017 · 2017
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Wen, R.; Torkkola, K.; Narayanaswamy, B.; and Madeka, D. 2017 · 2017
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Du, D.; Su, B.; and Wei, Z. 2022 · 2022
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