A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting
Slawek Smyl
International Journal of Forecasting, 2020, vol. 36, issue 1, 75-85
Abstract:
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.
Keywords: Forecasting competitions; M4; Dynamic computational graphs; Automatic differentiation; Long short term memory (LSTM) networks; Exponential smoothing (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:36:y:2020:i:1:p:75-85
DOI: 10.1016/j.ijforecast.2019.03.017
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