A hybrid method of Exponential Smoothing and Recurrent Neural Networks for time series forecasting

Code

• github.com/slaweks17/ES_RNN
  none★ 0
• github.com/kdgutier/esrnn_torch
  pytorch★ 0

Abstract

This paper presents the winning submission of the M4 forecasting competition. The submission utilizes a Dynamic Computational Graph Neural Network system that enables mixing of a standard Exponential Smoothing model 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

Tasks

Reproductions