A hybrid method of Exponential Smoothing and Recurrent Neural Networks for time series forecasting
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
PDF AbstractCode
Datasets
Add Datasets
introduced or used in this paper
Results from the Paper
Submit
results from this paper
to get state-of-the-art GitHub badges and help the
community compare results to other papers.
