Affiliations: [a] Department of Information Engineering, Fuzhou Polytechnic, Fuzhou, Fujian, China | [b] College of Information Engineering, Tarim University, Alar, Xinjiang, China
Correspondence:
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Corresponding author: Yongzhi Liu, Department of Information Engineering, Fuzhou Polytechnic, Fuzhou 350108, Fujian, China. E-mail: [email protected].
Abstract: An algorithm based on EMD-LSTM (Empirical Mode Decision – Long Short Term Memory) is proposed for predicting short time series with uncertainty, rapid changes, and no following cycle. First, the algorithm eliminates the abnormal data; second, the processed time series are decomposed into basic modal components for different characteristic scales, which can be used for further prediction; finally, an LSTM neural network is used to predict each modal component, and the prediction results for each modal component are summed to determine a final prediction. Experiments are performed on the public datasets available at UCR and compared with a machine learning algorithm based on LSTMs and SVMs. Several experiments have shown that the proposed EMD-LSTM-based short-time series prediction algorithm performs better than LSTM and SVM prediction methods and provides a feasible method for predicting short-time series.
