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In order to address these mentioned issues, enhanced machine learning algorithms can be employed to establish stock forecasting algorithms. Accordingly, introducing the idea of \u201cdecomposition and ensemble\u201d and the theory of \u201cgranular computing\u201d, a hybrid model in this paper is established by incorporating the complementary ensemble empirical mode decomposition (CEEMD), sample entropy (SE), independent component analysis (ICA), particle swarm optimization (PSO), and long short-term memory (LSTM). First, aiming at reducing the complexity of the original data of stock price, the CEEMD approach decomposes the data into different intrinsic mode functions (IMFs). To alleviate the cumulative error of IMFs, SE is performed to restructure the IMFs. Second, the ICA technique separates IMFs, describing the internal foundation structure. Finally, the LSTM model is adopted for forecasting the stock price results, in which the LSTM hyperparameters are optimized by synchronously utilizing the PSO algorithm. The experimental results on four stock prices from China stock market reveal the accuracy and robustness of the established model from the aspect of statistical efficiency measures. In theory, a useful attempt is made by integrating the idea of \u201cgranular computing\u201d with \u201cdecomposition and ensemble\u201d to construct the forecasting model of non-stationary data. In practice, the research results will provide scientific reference for the business community and researchers.<\/jats:p>","DOI":"10.1007\/s44196-022-00140-2","type":"journal-article","created":{"date-parts":[[2022,9,12]],"date-time":"2022-09-12T08:02:50Z","timestamp":1662969770000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["A Stock Price Forecasting Model Integrating Complementary Ensemble Empirical Mode Decomposition and Independent Component Analysis"],"prefix":"10.1007","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0976-3659","authenticated-orcid":false,"given":"Youwei","family":"Chen","sequence":"first","affiliation":[]},{"given":"Pengwei","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Zhen","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Juncheng","family":"Bai","sequence":"additional","affiliation":[]},{"given":"Yuqi","family":"Guo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,12]]},"reference":[{"key":"140_CR1","first-page":"1544","volume":"102","author":"P Ghosh","year":"2021","unstructured":"Ghosh, P., Neufeld, A., Sahoo, J.K.: Forecasting directional movements of stock prices for intraday trading using LSTM and random forests. 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