{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T16:06:41Z","timestamp":1778083601517,"version":"3.51.4"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,1,3]],"date-time":"2025-01-03T00:00:00Z","timestamp":1735862400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,1,3]],"date-time":"2025-01-03T00:00:00Z","timestamp":1735862400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Major Humanities and Social Sciences Research Projects in Zhejiang higher education institutions","award":["2023qn082"],"award-info":[{"award-number":["2023qn082"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61902349"],"award-info":[{"award-number":["61902349"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["EPJ Data Sci."],"DOI":"10.1140\/epjds\/s13688-024-00517-7","type":"journal-article","created":{"date-parts":[[2025,1,3]],"date-time":"2025-01-03T10:54:50Z","timestamp":1735901690000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["A hybrid stock prediction method based on periodic\/non-periodic features analyses"],"prefix":"10.1140","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-9655-6160","authenticated-orcid":false,"given":"Cheng","family":"Zhao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junyi","family":"Cai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuyi","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,1,3]]},"reference":[{"key":"517_CR1","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1007\/BF01414874","volume":"4","author":"K Kohara","year":"1996","unstructured":"Kohara K, Fukuhara Y, Nakamura Y (1996) Selective presentation learning for neural network forecasting of stock markets. Neural Comput Appl 4:143\u2013148","journal-title":"Neural Comput Appl"},{"key":"517_CR2","first-page":"77","volume":"53","author":"K-S Moon","year":"2019","unstructured":"Moon K-S, Kim H (2019) Performance of deep learning in prediction of stock market volatility. Econ Comput Econ Cybern Stud Res 53:77\u201392","journal-title":"Econ Comput Econ Cybern Stud Res"},{"key":"517_CR3","first-page":"62","volume":"2","author":"G Bontempi","year":"2013","unstructured":"Bontempi G, Ben Taieb S, Le Borgne Y-A (2013) Machine learning strategies for time series forecasting. Business intelligence: second European summer school, eBISS 2012, Brussels, Belgium, July 15-21, 2012. Tutorial Lect 2:62\u201377","journal-title":"Tutorial Lect"},{"issue":"1","key":"517_CR4","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1007\/s11071-019-04974-y","volume":"97","author":"JM Polanco-Mart\u00ednez","year":"2019","unstructured":"Polanco-Mart\u00ednez JM (2019) Dynamic relationship analysis between nafta stock markets using nonlinear, nonparametric, non-stationary methods. Nonlinear Dyn 97(1):369\u2013389","journal-title":"Nonlinear Dyn"},{"key":"517_CR5","doi-asserted-by":"publisher","first-page":"561","DOI":"10.1002\/for.1009","volume":"25","author":"A Wilhelmsson","year":"2006","unstructured":"Wilhelmsson A (2006) Garch forecasting performance under different distribution assumptions. J Forecast 25:561\u2013578","journal-title":"J Forecast"},{"key":"517_CR6","doi-asserted-by":"publisher","first-page":"3867","DOI":"10.1109\/SMC.2014.6974534","volume-title":"2014 IEEE international conference on Systems, Man, and Cybernetics (SMC)","author":"JF Lorenzato de Oliveira","year":"2014","unstructured":"Lorenzato de Oliveira JF, Ludermir TB (2014) A distributed pso-arima-svr hybrid system for time series forecasting. In: 2014 IEEE international conference on Systems, Man, and Cybernetics (SMC), pp\u00a03867\u20133872"},{"key":"517_CR7","first-page":"110","volume":"18","author":"X Xu","year":"2001","unstructured":"Xu X, Chen Y (2001) Empirical study on nonlinearity in China stock market. Quantit Tech Econ 18:110\u2013113","journal-title":"Quantit Tech Econ"},{"issue":"1","key":"517_CR8","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1016\/S0925-2312(03)00372-2","volume":"55","author":"K-j Kim","year":"2003","unstructured":"Kim K-j (2003) Financial time series forecasting using support vector machines. Neurocomputing 55(1):307\u2013319","journal-title":"Neurocomputing"},{"issue":"7553","key":"517_CR9","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436\u2013444","journal-title":"Nature"},{"key":"517_CR10","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. Adv Neural Inf Process Syst 30"},{"key":"517_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.119020","volume":"213","author":"F Zhou","year":"2023","unstructured":"Zhou F, Zhang Q, Zhu Y, Li T (2023) T2v_tf: an adaptive timing encoding mechanism based transformer with multi-source heterogeneous information fusion for portfolio management: a case of the Chinese a50 stocks. Expert Syst Appl 213:119020","journal-title":"Expert Syst Appl"},{"key":"517_CR12","unstructured":"Kazemi SM, Goel R, Eghbali S, Ramanan J, Sahota J, Thakur S, Wu S, Smyth C, Poupart P, Brubaker M (2019) Time2vec: Learning a vector representation of time. arXiv preprint. arXiv:1907.05321"},{"key":"517_CR13","doi-asserted-by":"publisher","first-page":"840","DOI":"10.1016\/j.energy.2018.09.118","volume":"165","author":"K Wang","year":"2018","unstructured":"Wang K, Qi X, Liu H, Song J (2018) Deep belief network based k-means cluster approach for short-term wind power forecasting. Energy 165:840\u2013852","journal-title":"Energy"},{"key":"517_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2022.104908","volume":"113","author":"J Wang","year":"2022","unstructured":"Wang J, Cui Q, Sun X, He M (2022) Asian stock markets closing index forecast based on secondary decomposition, multi-factor analysis and attention-based lstm model. Eng Appl Artif Intell 113:104908","journal-title":"Eng Appl Artif Intell"},{"key":"517_CR15","doi-asserted-by":"crossref","unstructured":"Masset P (2008) Analysis of financial time-series using Fourier and wavelet methods. Available at SSRN 1289420","DOI":"10.2139\/ssrn.1289420"},{"key":"517_CR16","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1007\/3-540-45718-6_35","volume-title":"Computational science\u00a0- ICCS 2001","author":"KK Teo","year":"2001","unstructured":"Teo KK, Wang L, Lin Z (2001) Wavelet packet multi-layer perceptron for chaotic time series prediction: effects of weight initialization. In: Computational science\u00a0- ICCS 2001. Springer, Berlin, pp\u00a0310\u2013317"},{"issue":"1971","key":"517_CR17","doi-asserted-by":"publisher","first-page":"903","DOI":"10.1098\/rspa.1998.0193","volume":"454","author":"NE Huang","year":"1998","unstructured":"Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Yen N-C, Tung CC, Liu HH (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc Lond, Ser A, Math Phys Eng Sci 454(1971):903\u2013995","journal-title":"Proc R Soc Lond, Ser A, Math Phys Eng Sci"},{"key":"517_CR18","doi-asserted-by":"publisher","first-page":"3285","DOI":"10.1109\/BigData47090.2019.9005997","volume-title":"2019 IEEE international conference on big data (big data)","author":"S Siami-Namini","year":"2019","unstructured":"Siami-Namini S, Tavakoli N, Namin AS (2019) The performance of lstm and bilstm in forecasting time series. In: 2019 IEEE international conference on big data (big data). IEEE, pp\u00a03285\u20133292"},{"key":"517_CR19","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1109\/ICAIBD.2019.8837038","volume-title":"2019 2nd International Conference on Artificial Intelligence and Big Data (ICAIBD)","author":"L Chen","year":"2019","unstructured":"Chen L, Chi Y, Guan Y, Fan J (2019) A hybrid attention-based emd-lstm model for financial time series prediction. In: 2019 2nd International Conference on Artificial Intelligence and Big Data (ICAIBD). IEEE, pp\u00a0113\u2013118"},{"key":"517_CR20","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1109\/ICAIBD.2019.8837038","volume-title":"2019 2nd International Conference on Artificial Intelligence and Big Data (ICAIBD)","author":"L Chen","year":"2019","unstructured":"Chen L, Chi Y, Guan Y, Fan J (2019) A hybrid attention-based emd-lstm model for financial time series prediction. In: 2019 2nd International Conference on Artificial Intelligence and Big Data (ICAIBD), pp\u00a0113\u2013118"},{"key":"517_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2023.110356","volume":"142","author":"Y Yao","year":"2023","unstructured":"Yao Y, Zhang Z-y, Zhao Y (2023) Stock index forecasting based on multivariate empirical mode decomposition and temporal convolutional networks. Appl Soft Comput 142:110356","journal-title":"Appl Soft Comput"},{"key":"517_CR22","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1016\/j.knosys.2018.10.034","volume":"164","author":"W Long","year":"2019","unstructured":"Long W, Lu Z, Cui L (2019) Deep learning-based feature engineering for stock price movement prediction. Knowl-Based Syst 164:163\u2013173","journal-title":"Knowl-Based Syst"},{"issue":"1","key":"517_CR23","first-page":"19","volume":"35","author":"T Kaczmarek","year":"2022","unstructured":"Kaczmarek T, Perez K (2022) Building portfolios based on machine learning predictions. Econ Res 35(1):19\u201337","journal-title":"Econ Res"},{"key":"517_CR24","doi-asserted-by":"publisher","first-page":"406","DOI":"10.1016\/j.procs.2014.05.284","volume":"31","author":"H Yu","year":"2014","unstructured":"Yu H, Chen R, Zhang G (2014) A svm stock selection model within pca. Proc Comput Sci 31:406\u2013412","journal-title":"Proc Comput Sci"},{"key":"517_CR25","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1109\/NILES50944.2020.9257950","volume-title":"2020 2nd Novel Intelligent and Leading Emerging Sciences conference (NILES)","author":"MAI Sunny","year":"2020","unstructured":"Sunny MAI, Maswood MMS, Alharbi AG (2020) Deep learning-based stock price prediction using lstm and bi-directional lstm model. In: 2020 2nd Novel Intelligent and Leading Emerging Sciences conference (NILES). IEEE, pp\u00a087\u201392"},{"issue":"1","key":"517_CR26","doi-asserted-by":"publisher","DOI":"10.3390\/asi4010009","volume":"4","author":"Z Hu","year":"2021","unstructured":"Hu Z, Zhao Y, Khushi M (2021) A survey of forex and stock price prediction using deep learning. Appl Syst Innov 4(1):9","journal-title":"Appl Syst Innov"},{"key":"517_CR27","volume-title":"Applying deep learning to enhance momentum trading strategies in stocks","author":"L Takeuchi","year":"2013","unstructured":"Takeuchi L, Lee Y-YA (2013) Applying deep learning to enhance momentum trading strategies in stocks. Stanford University, Stanford"},{"issue":"9","key":"517_CR28","doi-asserted-by":"publisher","first-page":"1449","DOI":"10.1080\/14697688.2019.1622295","volume":"19","author":"J Sirignano","year":"2019","unstructured":"Sirignano J, Cont R (2019) Universal features of price formation in financial markets: perspectives from deep learning. Quant Finance 19(9):1449\u20131459","journal-title":"Quant Finance"},{"key":"517_CR29","unstructured":"Kaushik M, Giri A (2020) Forecasting foreign exchange rate: a multivariate comparative analysis between traditional econometric, contemporary machine learning & deep learning techniques. arXiv preprint. arXiv:2002.10247"},{"key":"517_CR30","doi-asserted-by":"publisher","first-page":"1643","DOI":"10.1109\/ICACCI.2017.8126078","volume-title":"2017 International Conference on Advances in Computing, Communications and Informatics (icacci)","author":"S Selvin","year":"2017","unstructured":"Selvin S, Vinayakumar R, Gopalakrishnan E, Menon VK, Soman K (2017) Stock price prediction using lstm, rnn and cnn-sliding window model. In: 2017 International Conference on Advances in Computing, Communications and Informatics (icacci). IEEE, pp\u00a01643\u20131647"},{"key":"517_CR31","unstructured":"Li S, Jin X, Xuan Y, Zhou X, Chen W, Wang Y-X, Yan X (2019) Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting. Adv Neural Inf Process Syst 32"},{"key":"517_CR32","first-page":"11106","volume-title":"Proceedings of the AAAI conference on artificial intelligence","author":"H Zhou","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: Proceedings of the AAAI conference on artificial intelligence, vol\u00a035, pp\u00a011106\u201311115"},{"key":"517_CR33","doi-asserted-by":"crossref","unstructured":"Muhammad T, Aftab AB, Ahsan M, Muhu MM, Ibrahim M, Khan SI, Alam MS, et al (2022) Transformer-based deep learning model for stock price prediction: a case study on Bangladesh stock market. arXiv preprint. arXiv:2208.08300","DOI":"10.1142\/S146902682350013X"},{"key":"517_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.118128","volume":"208","author":"C Wang","year":"2022","unstructured":"Wang C, Chen Y, Zhang S, Zhang Q (2022) Stock market index prediction using deep transformer model. Expert Syst Appl 208:118128","journal-title":"Expert Syst Appl"},{"key":"517_CR35","first-page":"4640","volume-title":"IJCAI","author":"Q Ding","year":"2020","unstructured":"Ding Q, Wu S, Sun H, Guo J, Guo J (2020) Hierarchical multi-scale Gaussian transformer for stock movement prediction. In: IJCAI, pp\u00a04640\u20134646"},{"key":"517_CR36","doi-asserted-by":"crossref","unstructured":"Malibari N, Katib I, Mehmood R (2021) Predicting stock closing prices in emerging markets with transformer neural networks: the saudi stock exchange case. Int J Adv Comput Sci Appl 12(12)","DOI":"10.14569\/IJACSA.2021.01212106"},{"key":"517_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.114332","volume":"169","author":"H Rezaei","year":"2021","unstructured":"Rezaei H, Faaljou H, Mansourfar G (2021) Stock price prediction using deep learning and frequency decomposition. Expert Syst Appl 169:114332","journal-title":"Expert Syst Appl"},{"key":"517_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.sftr.2019.100003","volume":"2","author":"L Xian","year":"2020","unstructured":"Xian L, He K, Wang C, Lai KK (2020) Factor analysis of financial time series using eemd-ica based approach. Sustain Futures 2:100003","journal-title":"Sustain Futures"},{"key":"517_CR39","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1016\/j.neunet.2017.03.004","volume":"90","author":"J Wang","year":"2017","unstructured":"Wang J, Wang J (2017) Forecasting stochastic neural network based on financial empirical mode decomposition. Neural Netw 90:8\u201320","journal-title":"Neural Netw"},{"issue":"3","key":"517_CR40","first-page":"73","volume":"16","author":"H Dang","year":"2022","unstructured":"Dang H, Mei B (2022) Stock movement prediction using price factor and deep learning. Int J Comput Inf Eng 16(3):73\u201376","journal-title":"Int J Comput Inf Eng"},{"key":"517_CR41","doi-asserted-by":"publisher","first-page":"4144","DOI":"10.1109\/ICASSP.2011.5947265","volume-title":"2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","author":"ME Torres","year":"2011","unstructured":"Torres ME, Colominas MA, Schlotthauer G, Flandrin P (2011) A complete ensemble empirical mode decomposition with adaptive noise. In: 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, pp\u00a04144\u20134147"},{"key":"517_CR42","unstructured":"Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. arXiv preprint. arXiv:1301.3781"},{"key":"517_CR43","unstructured":"Tallec C, Ollivier Y (2018) Can recurrent neural networks warp time? arXiv preprint. arXiv:1804.11188"},{"key":"517_CR44","unstructured":"Mnih V, Heess N, Graves A, et al (2014) Recurrent models of visual attention. Adv Neural Inf Process Syst 27"},{"key":"517_CR45","unstructured":"JoinQuant (2014) JoinQuant Quantitative Investment Platform. Accessed: 2024-09-21. https:\/\/www.joinquant.com"},{"issue":"2","key":"517_CR46","first-page":"70","volume":"32","author":"A Sovi\u0107","year":"2012","unstructured":"Sovi\u0107 A, Ser\u0161i\u0107 D (2012) Signal decomposition methods for reducing drawbacks of the dwt. Eng Rev 32(2):70\u201377","journal-title":"Eng Rev"},{"issue":"1","key":"517_CR47","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1086\/294846","volume":"39","author":"WF Sharpe","year":"1966","unstructured":"Sharpe WF (1966) Mutual fund performance. J Bus 39(1):119\u2013138","journal-title":"J Bus"},{"issue":"6","key":"517_CR48","doi-asserted-by":"publisher","first-page":"1545","DOI":"10.1111\/j.1468-0262.2006.00718.x","volume":"74","author":"R Giacomini","year":"2006","unstructured":"Giacomini R, White H (2006) Tests of conditional predictive ability. Econometrica 74(6):1545\u20131578","journal-title":"Econometrica"},{"key":"517_CR49","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1016\/j.eswa.2019.03.029","volume":"129","author":"E Hoseinzade","year":"2019","unstructured":"Hoseinzade E, Haratizadeh S (2019) Cnnpred: cnn-based stock market prediction using a diverse set of variables. Expert Syst Appl 129:273\u2013285","journal-title":"Expert Syst Appl"},{"key":"517_CR50","doi-asserted-by":"crossref","unstructured":"Wu JM-T, Li Z, Herencsar N, Vo B, Lin JC-W (2021) A graph-based cnn-lstm stock price prediction algorithm with leading indicators. Multimed Syst:1\u201320","DOI":"10.1007\/s00530-021-00758-w"},{"key":"517_CR51","doi-asserted-by":"publisher","first-page":"4741","DOI":"10.1007\/s00521-020-05532-z","volume":"33","author":"W Lu","year":"2021","unstructured":"Lu W, Li J, Wang J, Qin L (2021) A cnn-bilstm-am method for stock price prediction. Neural Comput Appl 33:4741\u20134753","journal-title":"Neural Comput Appl"}],"container-title":["EPJ Data Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1140\/epjds\/s13688-024-00517-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1140\/epjds\/s13688-024-00517-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1140\/epjds\/s13688-024-00517-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,3]],"date-time":"2025-01-03T11:03:10Z","timestamp":1735902190000},"score":1,"resource":{"primary":{"URL":"https:\/\/epjdatascience.springeropen.com\/articles\/10.1140\/epjds\/s13688-024-00517-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,3]]},"references-count":51,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["517"],"URL":"https:\/\/doi.org\/10.1140\/epjds\/s13688-024-00517-7","relation":{},"ISSN":["2193-1127"],"issn-type":[{"value":"2193-1127","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,3]]},"assertion":[{"value":"21 June 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 December 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 January 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"1"}}