A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting

被引：329

作者：

Smyl, Slawek ^{[1
]}

机构：

[1] Uber Technol, 555 Market St, San Francisco, CA 94104 USA

来源：

INTERNATIONAL JOURNAL OF FORECASTING | 2020年 / 36卷 / 01期

关键词：

Forecasting competitions; M4; Dynamic computational graphs; Automatic differentiation; Long short term memory (LSTM) networks; Exponential smoothing; OF-THE-ART;

D O I：

10.1016/j.ijforecast.2019.03.017

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

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.

引用

页码：75 / 85

页数：11

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