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

Smyl Slawek

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

机译：时间序列预测的指数平滑和递归神经网络混合方法

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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.

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《International journal of forecasting》 |2020年第1期|75-85|共11页
作者
Smyl Slawek;
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作者单位

Uber Technol 555 Market St San Francisco CA 94104 USA;

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正文语种 eng
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关键词
Forecasting competitions; M4; Dynamic computational graphs; Automatic differentiation; Long short term memory (LSTM) networks; Exponential smoothing;

机译：预测比赛;M4;动态计算图;自动区分;长期短期记忆（LSTM）网络;指数平滑;

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6. Time series prediction of under-five mortality rates for Nigeria: comparative analysis of artificial neural networks Holt-Winters exponential smoothing and autoregressive integrated moving average models [O] . Daniel Adedayo Adeyinka, Nazeem Muhajarine 2020

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

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