期刊
INTERNATIONAL JOURNAL OF FORECASTING
卷 36, 期 1, 页码 75-85出版社
ELSEVIER
DOI: 10.1016/j.ijforecast.2019.03.017
关键词
Forecasting competitions; M4; Dynamic computational graphs; Automatic differentiation; Long short term memory (LSTM) networks; Exponential smoothing
This paper presents the winning submission of the M4 forecasting competition. The submission utilizes a dynamic computational graph neural network system that enables a standard exponential smoothing model to be mixed with advanced long short term memory networks into a common framework. The result is a hybrid and hierarchical forecasting method. (C) 2019 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
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