Fetching the paper…
Reading the bibliography…
Global Forecasting Models (GFM) that are trained across a set of multiple time series have shown superior results in many forecasting competitions and real-world applications compared with univariate forecasting approaches.
Exponential smoothing for predicting demand
Robert G Brown · 1957
Earlier work this paper cites.
Forecasting sales by exponentially weighted moving averages
Peter R Winters · 1960
Earlier work this paper cites.
The fundamental theorem of exponential smoothing
Robert G Brown and Richard F Meyer · 1961
Earlier work this paper cites.
The jackknife and the bootstrap for general stationary observations
Hans R Kunsch · 1989
Earlier work this paper cites.
STL: A seasonal-trend decomposition procedure based on loess
R. B. Cleveland, W. S. Cleveland, J.E. McRae, and I. Terpenning · 1990
Earlier work this paper cites.
Finding structure in time
Jeffrey L Elman · 1990
Earlier work this paper cites.
10 Structural time series models , volume 11 of Handbook of Statistics
Andrew C. Harvey and Neil Shephard · 1993
Earlier work this paper cites.
Sieve bootstrap for time series
Peter Bühlmann · 1997
Earlier work this paper cites.
The theta model: a decomposition approach to forecasting
Vassilis Assimakopoulos and Konstantinos Nikolopoulos · 2000
Earlier work this paper cites.
Learning to forget: Continual prediction with LSTM
Felix A. Gers, Jurgen Schmidhuber, and Fred Cummins · 2000
Earlier work this paper cites.
The M3-Competition: results, conclusions and implications
Spyros Makridakis and Michele Hibon · 2000
Earlier work this paper cites.
Tapered block bootstrap
Efstathios Paparoditis and Dimitris N Politis · 2001
Earlier work this paper cites.
Unmasking the theta method
Rob J Hyndman and Baki Billah · 2003
Earlier work this paper cites.
Another look at measures of forecast accuracy
Rob J Hyndman and Anne B Koehler · 2006
Earlier work this paper cites.
Automatic time series forecasting: the forecast package for R
Yeasmin Khandakar and Rob J Hyndman · 2008
Earlier work this paper cites.
Forecasting time series with BOOT.EXPOS procedure
Clara Cordeiro and M Neves · 2009
Earlier work this paper cites.
Banded and tapered estimates for autocovariance matrices and the linear process bootstrap
Timothy L McMurry and Dimitris N Politis · 2010
Earlier work this paper cites.
Forecasting time series with complex seasonal patterns using exponential smoothing
Alysha M. De Livera, Rob J. Hyndman, and Ralph D. Snyder · 2011
Earlier work this paper cites.
Sequential model-based optimization for general algorithm configuration
Frank Hutter, Holger H Hoos, and Kevin Leyton-Brown · 2011
Cited alongside, same era.
A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition
Souhaib Ben Taieb, Gianluca Bontempi, Amir F Atiya, and Antti Sorjamaa · 2012
Cited alongside, same era.
Bagging exponential smoothing methods using STL decomposition and Box–Cox transformation
Christoph Bergmeir, Rob J Hyndman, and José M Benítez · 2016
Cited alongside, same era.
Why should I trust you?: Explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
Cited alongside, same era.
Data preprocessing and augmentation for multiple short time series forecasting with recurrent neural networks
Slawek Smyl and Karthik Kuber · 2016
Cited alongside, same era.
BIM: Towards quantitative evaluation of interpretability methods with ground truth
Mengjiao Yang and Been Kim · 2019
Later among the works it cites.
Solar home electricity data, 2019
AusGrid · 2020
Later among the works it cites.
Forecasting across time series databases using recurrent neural networks on groups of similar series: A clustering approach
Kasun Bandara, Christoph Bergmeir, and Slawek Smyl · 2020
Later among the works it cites.
Caltrans PeMS, 2020
Caltrans · 2020
Later among the works it cites.
Web traffic time series forecasting, 2017
Google · 2020
Later among the works it cites.
forecast: Forecasting functions for time series and linear models , 2020
Rob Hyndman, George Athanasopoulos, Christoph Bergmeir, Gabriel Caceres, Leanne Chhay, Mitchell O’Hara-Wild, Fotios Petropoulos, Slava Razbash, Earo Wang, and Farah Yasmeen · 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Finale Doshi-Velez and Been Kim · 2017
Cited alongside, same era.
Time-series extreme event forecasting with neural networks at Uber
Nikolay Laptev, Jason Yosinski, Li Erran Li, and Slawek Smyl · 2017
Cited alongside, same era.
A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 2017
Cited alongside, same era.
Training deep networks without learning rates through coin betting
Francesco Orabona and Tatiana Tommasi · 2017
Cited alongside, same era.
A multi-horizon quantile recurrent forecaster
Ruofeng Wen, Kari Torkkola, Balakrishnan Narayanaswamy, and Dhruv Madeka · 2017
Cited alongside, same era.
Local rule-based explanations of black box decision systems
Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Dino Pedreschi, Franco Turini, and Fosca Giannotti · 2018
Cited alongside, same era.
Statistical and machine learning forecasting methods: Concerns and ways forward
Spyros Makridakis, Evangelos Spiliotis, and Vassilios Assimakopoulos · 2018
Cited alongside, same era.
Later among the works it cites.
Criteria for classifying forecasting methods
Tim Januschowski, Jan Gasthaus, Yuyang Wang, David Salinas, Valentin Flunkert, Michael Bohlke-Schneider, and Laurent Callot · 2020
Later among the works it cites.
The M5 accuracy competition: Results, findings and conclusions
S Makridakis, E Spiliotis, and V Assimakopoulos · 2020
Later among the works it cites.
N-beats: Neural basis expansion analysis for interpretable time series forecasting
Boris N Oreshkin, Dmitri Carpov, Nicolas Chapados, and Yoshua Bengio · 2020
Later among the works it cites.
LoRMIkA: local rule-based model interpretability with k-optimal associations
Dilini Rajapaksha, Christoph Bergmeir, and Wray Buntine · 2020
Later among the works it cites.
DeepAR: Probabilistic forecasting with autoregressive recurrent networks
David Salinas, Valentin Flunkert, Jan Gasthaus, and Tim Januschowski · 2020
Later among the works it cites.
A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting
Slawek Smyl · 2020
Later among the works it cites.
LSTM-MSNet: Leveraging forecasts on sets of related time series with multiple seasonal patterns
Kasun Bandara, Christoph Bergmeir, and Hansika Hewamalage · 2021
Closest in time.
Kaggle forecasting competitions: An overlooked learning opportunity
Casper Solheim Bojer and Jens Peder Meldgaard · 2021
Closest in time.
Recurrent neural networks for time series forecasting: Current status and future directions
Hansika Hewamalage, Christoph Bergmeir, and Kasun Bandara · 2021
Closest in time.
Forecasting: principles and practice, 2nd edition
Rob J Hyndman and George Athanasopoulos · 2021
Closest in time.
Principles and algorithms for forecasting groups of time series: Locality and globality
Pablo Montero-Manso and Rob J. Hyndman · 2021
Closest in time.
R: A language and environment for statistical computing
R Core Team · 2021
Closest in time.