Web: http://arxiv.org/abs/2009.14799

Jan. 28, 2022, 2:11 a.m. | Carson Eisenach, Yagna Patel, Dhruv Madeka

cs.LG updates on arXiv.org arxiv.org

Recent advances in neural forecasting have produced major improvements in
accuracy for probabilistic demand prediction. In this work, we propose novel
improvements to the current state of the art by incorporating changes inspired
by recent advances in Transformer architectures for Natural Language
Processing. We develop a novel decoder-encoder attention for context-alignment,
improving forecasting accuracy by allowing the network to study its own history
based on the context for which it is producing a forecast. We also present a
novel positional …

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