{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T14:02:29Z","timestamp":1787752949200,"version":"build-2784847793"},"reference-count":16,"publisher":"European Society of Computational Methods in Sciences and Engineering","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"published-print":{"date-parts":[[2023,10,6]]},"abstract":"<jats:p>An algorithm based on EMD-LSTM (Empirical Mode Decision\u00a0\u2013 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.<\/jats:p>","DOI":"10.3233\/jcm-226860","type":"journal-article","created":{"date-parts":[[2023,6,13]],"date-time":"2023-06-13T10:19:11Z","timestamp":1686651551000},"page":"2511-2524","source":"Crossref","is-referenced-by-count":1,"title":["Research on the prediction of short time series based on EMD-LSTM"],"prefix":"10.66113","volume":"23","author":[{"given":"Yongzhi","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Information Engineering, Fuzhou Polytechnic, Fuzhou, Fujian, China"},{"name":"College of Information Engineering, Tarim University, Alar, Xinjiang, 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