论文标题
一种新的统一深度学习方法，具有分解重建 - 时间序列的时间序列预测
A New Unified Deep Learning Approach with Decomposition-Reconstruction-Ensemble Framework for Time Series Forecasting
论文作者
论文摘要
本文为时间序列预测问题提出了一种新的分解模式分解（VMD）深度学习方法。首先，采用VMD将原始时间序列分解为几个子信号。然后，应用卷积神经网络（CNN）来学习分解的子信号上的重建模式，以获得几个重建的子信号。最后，使用长期的短期内存（LSTM）网络用于预测分解子信号和重建子信号作为输入的时间序列。所提出的VMD-CNN-LSTM方法源自分解重建框架，并通过嵌入重建，单个预测和整体步骤来创新统一的深度学习方法。为了验证所提出方法的预测性能，引入了四个典型的时间序列数据集以进行经验分析。经验结果表明，所提出的方法在预测准确性方面始终优于基准方法，并且还表明，通过CNN获得的重建亚信号对于进一步提高预测性能至关重要。
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.
