{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T05:35:50Z","timestamp":1763444150618,"version":"3.45.0"},"reference-count":42,"publisher":"Emerald","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,11,18]]},"abstract":"<jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Against the backdrop of policy intervention in China\u2019s stock market, this paper proposes a prediction framework integrating frequency-domain multi-scale feature fusion with a multi-head attention mechanism for stock price forecasting.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Design\/methodology\/approach<\/jats:title>\n                    <jats:p>The adaptive noise complete ensemble empirical mode decomposition (CEEMDAN) algorithm is combined with the k-means clustering algorithm to generate feature sequences across different frequency bands. Through Pearson correlation coefficient analysis, feature sequences with strong relevance to target labels are selected as model inputs. These preprocessed features are fed into a hybrid architecture comprising a convolutional neural network (CNN), bidirectional long short-term memory network (BiLSTM) and multi-head attention layers.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Findings<\/jats:title>\n                    <jats:p>Extensive experiments on stock datasets from the Shanghai Stock Exchange (SHA) and Shenzhen Stock Exchange (SHE) demonstrate that the model achieves robust prediction accuracy and generalization capability and Welch\u2019s t-test is performed to validate the results statistically. It achieves better prediction results in most datasets. Specifically, in the SHE: 000,021 dataset, the MSE is 0.906 and the MAE is 0.698, which are significantly lower than those of the baseline model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Originality\/value<\/jats:title>\n                    <jats:p>This innovative approach integrating decomposition-aggregation algorithms with multi-head attention mechanisms offers a novel technical solution for stock market participants. The study concludes with a discussion on the model\u2019s advantages, limitations and potential optimization directions.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1108\/ijicc-05-2025-0266","type":"journal-article","created":{"date-parts":[[2025,9,5]],"date-time":"2025-09-05T16:15:29Z","timestamp":1757088929000},"page":"589-612","source":"Crossref","is-referenced-by-count":0,"title":["A hybrid neural framework for financial time series forecasting with CEEMDAN-CNN-BiLSTM-attention integration"],"prefix":"10.1108","volume":"18","author":[{"given":"Haoran","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Information and Mathematics, Yangtze University , ,","place":["Hubei, China"]}]},{"given":"Yueen","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Information and Mathematics, Yangtze University , ,","place":["Hubei, China"]}]},{"given":"Nianqi","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Economics and Management, Yangtze University , ,","place":["Hubei, China"]}]},{"given":"Junjie","family":"Du","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Jingzhou University , ,","place":["Hubei, China"]}]},{"given":"Xiaofei","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information and Mathematics, Yangtze University , ,","place":["Hubei, China"]}]}],"member":"140","published-online":{"date-parts":[[2025,9,8]]},"reference":[{"issue":"8","key":"2025111800322505700_ref001","doi-asserted-by":"crossref","first-page":"1295","DOI":"10.3390\/electronics9081295","article-title":"The k-means algorithm: a comprehensive survey and performance evaluation","volume":"9","author":"Ahmed","year":"2020","journal-title":"Electronics"},{"issue":"332","key":"2025111800322505700_ref002","doi-asserted-by":"crossref","first-page":"1509","DOI":"10.1080\/01621459.1970.10481180","article-title":"Distribution of residual autocorrelations in autoregressive-integrated moving average time series models","volume":"65","author":"Box","year":"1970","journal-title":"Journal of the American Statistical Association"},{"key":"2025111800322505700_ref003","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2024.130876","article-title":"Improving the accuracy of wind speed spatial interpolation: a pre-processing algorithm for wind speed dynamic time warping interpolation","volume":"295","author":"Chen","year":"2024","journal-title":"Energy"},{"key":"2025111800322505700_ref004","article-title":"A hybrid ceemdan-vmd-timesnet model for significant wave height prediction in the south sea of China","volume":"11","author":"Ding","year":"2024","journal-title":"Frontiers in Marine Science"},{"issue":"2","key":"2025111800322505700_ref005","doi-asserted-by":"crossref","first-page":"383","DOI":"10.2307\/2325486","article-title":"Efficient capital markets","volume":"25","author":"Fama","year":"1970","journal-title":"The Journal of Finance"},{"key":"2025111800322505700_ref006","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2024.107865","article-title":"A new filter feature selection algorithm for classification task by ensembling pearson correlation coefficient and mutual information","volume":"131","author":"Gong","year":"2024","journal-title":"Engineering Applications of Artificial Intelligence"},{"issue":"1971","key":"2025111800322505700_ref007","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1098\/rspa.1998.0193","article-title":"The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis","volume":"454","author":"Huang","year":"1998","journal-title":"Proceedings of the Royal Society of London. 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