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Hybrid deep learning and empirical mode decomposition model for time series applications

机译：时序应用的混合深度学习和经验模式分解模型

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Time series forecasting is important in many aspects of our lives, since it can be used to deal with the uncertainty to further support the decision making. Despite many advanced methodologies have been proposed, modelling the underlying relationship between past and future conditions is still a challenge. In this research, we develop a novel time series forecasting model which can effectively predict future conditions in a timely fashion. A time series has nonlinear and nonstationary characteristics which make prediction using statistical or computational intelligent methods a difficult task. Therefore, a hybrid deep learning and empirical mode decomposition model, namely EMD-SAE, for multistep ahead forecasting is proposed to predict the traffic flow and random time series. The performances of the proposed model are compared and discussed. This paper shows the potential of hybridizing the deep learning and empirical mode decomposition to the ordinary time series forecasting approach, and the experimental results suggest that the proposed EMD-SAE is reliable, suitable and a promising method for time series forecasting. (C) 2018 Elsevier Ltd. All rights reserved.
机译：时间序列预测在我们生活的许多方面都很重要，因为它可以用来处理不确定性，以进一步支持决策。尽管已经提出了许多先进的方法，但是对过去和将来条件之间的潜在关系进行建模仍然是一个挑战。在这项研究中，我们开发了一种新颖的时间序列预测模型，该模型可以及时有效地预测未来状况。时间序列具有非线性和非平稳特性，这使得使用统计或计算智能方法进行预测变得困难。因此，提出了一种混合的深度学习和经验模式分解模型，即EMD-SAE，用于多步提前预测，以预测交通流量和随机时间序列。比较和讨论了所提出模型的性能。本文显示了将深度学习和经验模式分解与常规时间序列预测方法相结合的潜力，并且实验结果表明，提出的EMD-SAE是可靠，合适且有希望的时间序列预测方法。 （C）2018 Elsevier Ltd.保留所有权利。

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