  A new variational mode decomposition (VMD) based deep learning approach is
proposed in this paper for time series forecasting problem. Firstly, VMD is
adopted to decompose the original time series into several sub-signals. Then, a
convolutional neural network (CNN) is applied to learn the reconstruction
patterns on the decomposed sub-signals to obtain several reconstructed
sub-signals. Finally, a long short term memory (LSTM) network is employed to
forecast the time series with the decomposed sub-signals and the reconstructed
sub-signals as inputs. The proposed VMD-CNN-LSTM approach is originated from
the decomposition-reconstruction-ensemble framework, and innovated by embedding
the reconstruction, single forecasting, and ensemble steps in a unified deep
learning approach. To verify the forecasting performance of the proposed
approach, four typical time series datasets are introduced for empirical
analysis. The empirical results demonstrate that the proposed approach
outperforms consistently the benchmark approaches in terms of forecasting
accuracy, and also indicate that the reconstructed sub-signals obtained by CNN
is of importance for further improving the forecasting performance.
