Realizing the accurate prediction of data flow is an important and challenging problem in industrial automation. However, due to the diversity of data types, it is difficult for traditional time series prediction models to have good prediction effects on different types of data. To improve the versatility and accuracy of the model, this paper proposes a novel hybrid time-series prediction model based on recursive empirical mode decomposition (REMD) and long short-term memory (LSTM). In REMD-LSTM, we first propose a new REMD to overcome the marginal effects and mode confusion problems in traditional decomposition methods. Then use REMD to decompose the data stream into multiple in intrinsic modal functions (IMF). After that, LSTM is used to predict each IMF subsequence separately and obtain the corresponding prediction results. Finally, the true prediction value of the input data is obtained by accumulating the prediction results of all IMF subsequences. The final experimental results show that the prediction accuracy of our proposed model is improved by more than 20% compared with the LSTM algorithm. In addition, the model has the highest prediction accuracy on all different types of data sets. This fully shows the model proposed in this paper has a greater advantage in prediction accuracy and versatility than the state-of-the-art models. The data used in the experiment can be downloaded from this website: https://github.com/Yang-Yun726/REMD-LSTM.

实现数据流的准确预测是工业自动化中一个重要且具有挑战性的问题。然而，由于数据类型的多样性，传统的时间序列预测模型很难对不同类型的数据产生良好的预测效果。为了提高模型的通用性和准确性，本文提出了一种基于递归经验模式分解（REMD）和长短期记忆（LSTM）的混合时间序列预测模型。在 REMD-LSTM 中，我们首先提出了一种新的 REMD，以克服传统分解方法中的边际效应和模式混淆问题。然后使用REMD将数据流分解为多个内在模态函数（IMF）。之后，利用LSTM分别对每个IMF子序列进行预测，得到相应的预测结果。最后，输入数据的真实预测值是通过对所有IMF子序列的预测结果进行累加得到的。最终的实验结果表明，与LSTM算法相比，我们提出的模型的预测精度提高了20%以上。此外，该模型在所有不同类型的数据集上具有最高的预测精度。这充分说明了本文提出的模型在预测精度和通用性上比state-of-the-art模型具有更大的优势。实验使用的数据可以从这个网站下载：https://github.com/Yang-Yun726/REMD-LSTM。最终的实验结果表明，与LSTM算法相比，我们提出的模型的预测精度提高了20%以上。此外，该模型在所有不同类型的数据集上具有最高的预测精度。这充分说明了本文提出的模型在预测精度和通用性上比state-of-the-art模型具有更大的优势。实验使用的数据可以从这个网站下载：https://github.com/Yang-Yun726/REMD-LSTM。最终的实验结果表明，与LSTM算法相比，我们提出的模型的预测精度提高了20%以上。此外，该模型在所有不同类型的数据集上具有最高的预测精度。这充分说明了本文提出的模型在预测精度和通用性上比state-of-the-art模型具有更大的优势。实验使用的数据可以从这个网站下载：https://github.com/Yang-Yun726/REMD-LSTM。