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Deep learning based forecasting methods have become the methods of choice in many applications of time series prediction or forecasting often outperforming other approaches.
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Bayesian intermittent demand forecasting for large inventories
Matthias W Seeger, David Salinas, and Valentin Flunkert · 2016
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Mastering the game of go with deep neural networks and tree search
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Quantile autoregression neural network model with applications to evaluating value at risk
Qifa Xu, Xi Liu, Cuixia Jiang, and Keming Yu · 2016
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A hybrid machine learning model for forecasting a billing period’s peak electric load days
Harshit Saxena, Omar Aponte, and Katie T. McConky · 2019
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Causality for machine learning
Bernhard Schölkopf · 2019
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Think globally, act locally: A deep neural network approach to high-dimensional time series forecasting
Rajat Sen, Hsiang-Fu Yu, and Inderjit S Dhillon · 2019
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Machine learning methods for GEFCom2017 probabilistic load forecasting
Slawek Smyl and N. Grace Hua · 2019
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Modeling financial time-series with generative adversarial networks
Shuntaro Takahashi, Yu Chen, and Kumiko Tanaka-Ishii · 2019
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Subutai Ahmad, Alexander Lavin, Scott Purdy, and Zuha Agha · 2017
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Forecasting with temporal hierarchies
George Athanasopoulos, Rob J Hyndman, Nikolaos Kourentzes, and Fotios Petropoulos · 2017
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Anastasia Borovykh, Sander Bohte, and Cornelis W Oosterlee · 2017
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Self-attention generative adversarial networks
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