{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:15:09Z","timestamp":1750220109042,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":23,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,6,23]],"date-time":"2022-06-23T00:00:00Z","timestamp":1655942400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,6,23]]},"DOI":"10.1145\/3548636.3548654","type":"proceedings-article","created":{"date-parts":[[2022,8,23]],"date-time":"2022-08-23T16:09:33Z","timestamp":1661270973000},"page":"117-124","source":"Crossref","is-referenced-by-count":0,"title":["Stock price prediction under multi-frequency model - based on attention state-frequency memory network"],"prefix":"10.1145","author":[{"given":"Wei","family":"Zhou","sequence":"first","affiliation":[{"name":"School of Finance, Yunnan University of Finance and Economics, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuting","family":"Pan","sequence":"additional","affiliation":[{"name":"School of Finance, Yunnan University of Finance and Economics, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhaoxia","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Finance, Yunnan University of Finance and Economics, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,8,23]]},"reference":[{"issue":"17","key":"e_1_3_2_1_1_1","first-page":"1566","article-title":"A random walk down wall street: including a life-cycle guide to personal investing","volume":"40","author":"Malkiel B. G.","year":"1990","unstructured":"Malkiel , B. G. ( 1990 ). A random walk down wall street: including a life-cycle guide to personal investing . Ww Norton & Company , 40 ( 17 ), 1566 . Malkiel, B. G. (1990). A random walk down wall street: including a life-cycle guide to personal investing. Ww Norton & Company, 40(17), 1566.","journal-title":"Ww Norton & Company"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1155\/2016\/1285768"},{"key":"e_1_3_2_1_3_1","volume-title":"In\u00a02016 IEEE\/ACIS 15th International Conference on Computer and Information Science (ICIS)\u00a0(pp. 1-6). IEEE.","author":"Akita R.","year":"2016","unstructured":"Akita , R. , Yoshihara , A. , Matsubara , T. , & Uehara , K. ( 2016 , June). Deep learning for stock prediction using numerical and textual information . In\u00a02016 IEEE\/ACIS 15th International Conference on Computer and Information Science (ICIS)\u00a0(pp. 1-6). IEEE. Akita, R., Yoshihara, A., Matsubara, T., & Uehara, K. (2016, June). Deep learning for stock prediction using numerical and textual information. In\u00a02016 IEEE\/ACIS 15th International Conference on Computer and Information Science (ICIS)\u00a0(pp. 1-6). IEEE."},{"key":"e_1_3_2_1_4_1","volume-title":"A Na\u00efve SVM-KNN based stock market trend reversal analysis for Indian benchmark indices.\u00a0Applied Soft Computing,\u00a035, 670-680","author":"Nayak R. K.","year":"2015","unstructured":"Nayak , R. K. , Mishra , D. , & Rath , A. K. ( 2015 ). A Na\u00efve SVM-KNN based stock market trend reversal analysis for Indian benchmark indices.\u00a0Applied Soft Computing,\u00a035, 670-680 . Nayak, R. K., Mishra, D., & Rath, A. K. (2015). A Na\u00efve SVM-KNN based stock market trend reversal analysis for Indian benchmark indices.\u00a0Applied Soft Computing,\u00a035, 670-680."},{"key":"e_1_3_2_1_5_1","first-page":"171","volume-title":"ANN and GA for stock market forecasting.\u00a0Expert systems with Applications,\u00a033(1)","author":"Hassan M. R.","year":"2007","unstructured":"Hassan , M. R. , Nath , B. , & Kirley , M. ( 2007 ). A fusion model of HMM , ANN and GA for stock market forecasting.\u00a0Expert systems with Applications,\u00a033(1) , 171 - 180 . Hassan, M. R., Nath, B., & Kirley, M. (2007). A fusion model of HMM, ANN and GA for stock market forecasting.\u00a0Expert systems with Applications,\u00a033(1), 171-180."},{"key":"e_1_3_2_1_6_1","volume-title":"Simultaneous optimization of artificial neural networks for financial forecasting.\u00a0Applied Intelligence,\u00a036(4), 887-898","author":"Kim K. J.","year":"2012","unstructured":"Kim , K. J. , & Ahn , H. ( 2012 ). Simultaneous optimization of artificial neural networks for financial forecasting.\u00a0Applied Intelligence,\u00a036(4), 887-898 . Kim, K. J., & Ahn, H. (2012). Simultaneous optimization of artificial neural networks for financial forecasting.\u00a0Applied Intelligence,\u00a036(4), 887-898."},{"key":"e_1_3_2_1_7_1","volume-title":"Maximum and minimum stock price forecasting of Brazilian power distribution companies based on artificial neural networks.\u00a0Applied Soft Computing,\u00a035, 66-74","author":"Laboissiere L. A.","year":"2015","unstructured":"Laboissiere , L. A. , Fernandes , R. A. , & Lage , G. G. ( 2015 ). Maximum and minimum stock price forecasting of Brazilian power distribution companies based on artificial neural networks.\u00a0Applied Soft Computing,\u00a035, 66-74 . Laboissiere, L. A., Fernandes, R. A., & Lage, G. G. (2015). Maximum and minimum stock price forecasting of Brazilian power distribution companies based on artificial neural networks.\u00a0Applied Soft Computing,\u00a035, 66-74."},{"key":"e_1_3_2_1_8_1","volume-title":"Artificial neural networks\u2013an application to stock market volatility.\u00a0Soft-Computing in Capital Market: Research and Methods of Computational Finance for Measuring Risk of Financial Instruments,\u00a0179","author":"Mantri J. K.","year":"2014","unstructured":"Mantri , J. K. , Gahan , P. , & Nayak , B. B. ( 2014 ). Artificial neural networks\u2013an application to stock market volatility.\u00a0Soft-Computing in Capital Market: Research and Methods of Computational Finance for Measuring Risk of Financial Instruments,\u00a0179 . Mantri, J. K., Gahan, P., & Nayak, B. B. (2014). Artificial neural networks\u2013an application to stock market volatility.\u00a0Soft-Computing in Capital Market: Research and Methods of Computational Finance for Measuring Risk of Financial Instruments,\u00a0179."},{"key":"e_1_3_2_1_9_1","volume-title":"Predicting stock market index using fusion of machine learning techniques.\u00a0Expert Systems with Applications,\u00a042(4), 2162-2172","author":"Patel J.","year":"2015","unstructured":"Patel , J. , Shah , S. , Thakkar , P. , & Kotecha , K. ( 2015 ). Predicting stock market index using fusion of machine learning techniques.\u00a0Expert Systems with Applications,\u00a042(4), 2162-2172 . Patel, J., Shah, S., Thakkar, P., & Kotecha, K. (2015). Predicting stock market index using fusion of machine learning techniques.\u00a0Expert Systems with Applications,\u00a042(4), 2162-2172."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-642-24797-2","volume-title":"Long short-term memory.\u00a0Supervised sequence labelling with recurrent neural networks, 37-45","author":"Graves A.","year":"2012","unstructured":"Graves , A. ( 2012 ). Long short-term memory.\u00a0Supervised sequence labelling with recurrent neural networks, 37-45 . Graves, A. (2012). Long short-term memory.\u00a0Supervised sequence labelling with recurrent neural networks, 37-45."},{"key":"e_1_3_2_1_11_1","volume-title":"Attention is all you need.\u00a0Advances in neural information processing systems,\u00a030","author":"Vaswani A.","year":"2017","unstructured":"Vaswani , A. , Shazeer , N. , Parmar , N. , Uszkoreit , J. , Jones , L. , Gomez , A. N., . .. & Polosukhin , I. ( 2017 ). Attention is all you need.\u00a0Advances in neural information processing systems,\u00a030 . Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need.\u00a0Advances in neural information processing systems,\u00a030."},{"key":"e_1_3_2_1_12_1","volume-title":"Informer: Beyond efficient transformer for long sequence time-series forecasting. In\u00a0Proceedings of AAAI","author":"Zhou H.","year":"2021","unstructured":"Zhou , H. , Zhang , S. , Peng , J. , Zhang , S. , Li , J. , Xiong , H. , & Zhang , W. ( 2021 ). Informer: Beyond efficient transformer for long sequence time-series forecasting. In\u00a0Proceedings of AAAI . Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., & Zhang, W. (2021). Informer: Beyond efficient transformer for long sequence time-series forecasting. In\u00a0Proceedings of AAAI."},{"key":"e_1_3_2_1_13_1","volume-title":"Investigation of financial market prediction by recurrent neural network.\u00a0Innovative Technologies for Science, Business and Education,\u00a02(11), 3-8","author":"Maknickien\u0117 N.","year":"2011","unstructured":"Maknickien\u0117 , N. , Rutkauskas , A. V. , & Maknickas , A. ( 2011 ). Investigation of financial market prediction by recurrent neural network.\u00a0Innovative Technologies for Science, Business and Education,\u00a02(11), 3-8 . Maknickien\u0117, N., Rutkauskas, A. V., & Maknickas, A. (2011). Investigation of financial market prediction by recurrent neural network.\u00a0Innovative Technologies for Science, Business and Education,\u00a02(11), 3-8."},{"key":"e_1_3_2_1_14_1","volume-title":"Financial market prediction system with Evolino neural network and Delphi method.\u00a0Journal of Business Economics and Management,\u00a014(2), 403-413","author":"Maknickien\u0117 N.","year":"2013","unstructured":"Maknickien\u0117 , N. , & Maknickas , A. ( 2013 ). Financial market prediction system with Evolino neural network and Delphi method.\u00a0Journal of Business Economics and Management,\u00a014(2), 403-413 . Maknickien\u0117, N., & Maknickas, A. (2013). Financial market prediction system with Evolino neural network and Delphi method.\u00a0Journal of Business Economics and Management,\u00a014(2), 403-413."},{"key":"e_1_3_2_1_15_1","volume-title":"Recurrent neural network and a hybrid model for prediction of stock returns.\u00a0Expert Systems with Applications,\u00a042(6), 3234-3241","author":"Rather A. M.","year":"2015","unstructured":"Rather , A. M. , Agarwal , A. , & Sastry , V. N. ( 2015 ). Recurrent neural network and a hybrid model for prediction of stock returns.\u00a0Expert Systems with Applications,\u00a042(6), 3234-3241 . Rather, A. M., Agarwal, A., & Sastry, V. N. (2015). Recurrent neural network and a hybrid model for prediction of stock returns.\u00a0Expert Systems with Applications,\u00a042(6), 3234-3241."},{"key":"e_1_3_2_1_17_1","volume-title":"In\u00a02016 IEEE\/ACIS 15th International Conference on Computer and Information Science (ICIS)\u00a0(pp. 1-6). IEEE.","author":"Akita R.","year":"2016","unstructured":"Akita , R. , Yoshihara , A. , Matsubara , T. , & Uehara , K. ( 2016 , June). Deep learning for stock prediction using numerical and textual information . In\u00a02016 IEEE\/ACIS 15th International Conference on Computer and Information Science (ICIS)\u00a0(pp. 1-6). IEEE. Akita, R., Yoshihara, A., Matsubara, T., & Uehara, K. (2016, June). Deep learning for stock prediction using numerical and textual information. In\u00a02016 IEEE\/ACIS 15th International Conference on Computer and Information Science (ICIS)\u00a0(pp. 1-6). IEEE."},{"key":"e_1_3_2_1_18_1","volume-title":"Learning phrase representations using RNN encoder-decoder for statistical machine translation.\u00a0arXiv preprint arXiv:1406.1078","author":"Cho K.","year":"2014","unstructured":"Cho , K. , Van Merri\u00ebnboer , B. , Gulcehre , C. , Bahdanau , D. , Bougares , F. , Schwenk , H. , & Bengio , Y. ( 2014 ). Learning phrase representations using RNN encoder-decoder for statistical machine translation.\u00a0arXiv preprint arXiv:1406.1078 . Cho, K., Van Merri\u00ebnboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation.\u00a0arXiv preprint arXiv:1406.1078."},{"key":"e_1_3_2_1_19_1","volume-title":"In\u00a0Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN","author":"Gers F. A.","year":"2000","unstructured":"Gers , F. A. , & Schmidhuber , J. ( 2000 ). Recurrent nets that time and count . In\u00a0Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing : New Challenges and Perspectives for the New Millennium\u00a0(Vol. 3, pp. 189-194). IEEE. Gers, F. A., & Schmidhuber, J. (2000). Recurrent nets that time and count. In\u00a0Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium\u00a0(Vol. 3, pp. 189-194). IEEE."},{"key":"e_1_3_2_1_20_1","volume-title":"Framewise phoneme classification with bidirectional LSTM and other neural network architectures.\u00a0Neural networks,\u00a018(5-6), 602-610","author":"Graves A.","year":"2005","unstructured":"Graves , A. , & Schmidhuber , J. ( 2005 ). Framewise phoneme classification with bidirectional LSTM and other neural network architectures.\u00a0Neural networks,\u00a018(5-6), 602-610 . Graves, A., & Schmidhuber, J. (2005). Framewise phoneme classification with bidirectional LSTM and other neural network architectures.\u00a0Neural networks,\u00a018(5-6), 602-610."},{"key":"e_1_3_2_1_21_1","volume-title":"In\u00a0International Conference on Machine Learning\u00a0(pp. 1568-1577)","author":"Hu H.","year":"2017","unstructured":"Hu , H. , & Qi , G. J. ( 2017 , July). State-frequency memory recurrent neural networks . In\u00a0International Conference on Machine Learning\u00a0(pp. 1568-1577) . PMLR. Hu, H., & Qi, G. J. (2017, July). State-frequency memory recurrent neural networks. In\u00a0International Conference on Machine Learning\u00a0(pp. 1568-1577). PMLR."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"crossref","DOI":"10.1109\/TFUZZ.2022.3170657","article-title":"The dual-fuzzy convolutional neural network to deal with handwritten image recognition","author":"Zhou W.","year":"2022","unstructured":"Zhou , W. , Liu , M. , & Xu , Z. ( 2022 ). The dual-fuzzy convolutional neural network to deal with handwritten image recognition . IEEE Transactions on Fuzzy Systems. Zhou, W., Liu, M., & Xu, Z. (2022). The dual-fuzzy convolutional neural network to deal with handwritten image recognition. IEEE Transactions on Fuzzy Systems.","journal-title":"IEEE Transactions on Fuzzy Systems."},{"key":"e_1_3_2_1_23_1","volume-title":"Attention is all you need. Advances in neural information processing systems, 30","author":"Vaswani A.","year":"2017","unstructured":"Vaswani , A. , Shazeer , N. , Parmar , N. , Uszkoreit , J. , Jones , L. , Gomez , A. N., . .. & Polosukhin , I. ( 2017 ). Attention is all you need. Advances in neural information processing systems, 30 . Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30."},{"key":"e_1_3_2_1_24_1","volume-title":"Machine learning methods based on probabilistic decision tree under the multi-valued preference environment. Economic Research-Ekonomska Istra\u017eivanja, 1-45","author":"Zhou W.","year":"2021","unstructured":"Zhou , W. , Lu , Y. , Liu , M. , & Zhang , K. ( 2021 ). Machine learning methods based on probabilistic decision tree under the multi-valued preference environment. Economic Research-Ekonomska Istra\u017eivanja, 1-45 . Zhou, W., Lu, Y., Liu, M., & Zhang, K. (2021). Machine learning methods based on probabilistic decision tree under the multi-valued preference environment. Economic Research-Ekonomska Istra\u017eivanja, 1-45."}],"event":{"name":"ITCC 2022: 2022 4th International Conference on Information Technology and Computer Communications","acronym":"ITCC 2022","location":"Guangzhou China"},"container-title":["2022 4th International Conference on Information Technology and Computer Communications (ITCC)"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3548636.3548654","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3548636.3548654","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T18:10:39Z","timestamp":1750183839000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3548636.3548654"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,23]]},"references-count":23,"alternative-id":["10.1145\/3548636.3548654","10.1145\/3548636"],"URL":"https:\/\/doi.org\/10.1145\/3548636.3548654","relation":{},"subject":[],"published":{"date-parts":[[2022,6,23]]}}}