{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T07:14:47Z","timestamp":1777965287101,"version":"3.51.4"},"reference-count":58,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2024,5,15]],"date-time":"2024-05-15T00:00:00Z","timestamp":1715731200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,5,15]],"date-time":"2024-05-15T00:00:00Z","timestamp":1715731200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"The fundamental research program of Guangdong, China","award":["2020B1515310023"],"award-info":[{"award-number":["2020B1515310023"]}]},{"name":"The fundamental research program of Guangdong, China","award":["2023A1515011281"],"award-info":[{"award-number":["2023A1515011281"]}]},{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"crossref","award":["61571005"],"award-info":[{"award-number":["61571005"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"DOI":"10.1007\/s00521-024-09916-3","type":"journal-article","created":{"date-parts":[[2024,5,15]],"date-time":"2024-05-15T09:02:23Z","timestamp":1715763743000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["DeepAR-Attention probabilistic prediction for stock price series"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9368-5835","authenticated-orcid":false,"given":"Jiacheng","family":"Li","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0002-2880-3078","authenticated-orcid":false,"given":"Wei","family":"Chen","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4040-0175","authenticated-orcid":false,"given":"Zhiheng","family":"Zhou","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9677-0768","authenticated-orcid":false,"given":"Junmei","family":"Yang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7322-1873","authenticated-orcid":false,"given":"Delu","family":"Zeng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,5,15]]},"reference":[{"issue":"4","key":"9916_CR1","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1016\/0165-4101(89)90017-7","volume":"11","author":"JA Ou","year":"1989","unstructured":"Ou JA, Penman SH (1989) Financial statement analysis and the prediction of stock returns. J Account Econom 11(4):295\u2013329","journal-title":"J Account Econom"},{"key":"9916_CR2","doi-asserted-by":"crossref","unstructured":"Wang J-H, Leu J-Y (1996) Stock market trend prediction using arima-based neural networks. In: proceedings of international conference on neural networks (ICNN\u201996), vol. 4, pp. 2160\u20132165. IEEE","DOI":"10.1109\/ICNN.1996.549236"},{"key":"9916_CR3","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1007\/BF00994018","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273\u2013297","journal-title":"Mach Learn"},{"issue":"5","key":"9916_CR4","doi-asserted-by":"publisher","first-page":"5569","DOI":"10.1016\/j.eswa.2010.10.079","volume":"38","author":"L-P Ni","year":"2011","unstructured":"Ni L-P, Ni Z-W, Gao Y-Z (2011) Stock trend prediction based on fractal feature selection and support vector machine. Expert Syst Appl 38(5):5569\u20135576","journal-title":"Expert Syst Appl"},{"key":"9916_CR5","doi-asserted-by":"publisher","first-page":"805","DOI":"10.1007\/s00521-015-1897-9","volume":"27","author":"M Thenmozhi","year":"2016","unstructured":"Thenmozhi M, Sarath Chand G (2016) Forecasting stock returns based on information transmission across global markets using support vector machines. Neural Comput Appl 27:805\u2013824","journal-title":"Neural Comput Appl"},{"issue":"32","key":"9916_CR6","doi-asserted-by":"publisher","first-page":"23295","DOI":"10.1007\/s00521-021-05842-w","volume":"35","author":"RK Dash","year":"2023","unstructured":"Dash RK, Nguyen TN, Cengiz K, Sharma A (2023) Fine-tuned support vector regression model for stock predictions. Neural Comput Appl 35(32):23295\u201323309","journal-title":"Neural Comput Appl"},{"issue":"6088","key":"9916_CR7","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1038\/323533a0","volume":"323","author":"DE Rumelhart","year":"1986","unstructured":"Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323(6088):533\u2013536","journal-title":"Nature"},{"issue":"11","key":"9916_CR8","doi-asserted-by":"publisher","first-page":"14346","DOI":"10.1016\/j.eswa.2011.04.222","volume":"38","author":"J-Z Wang","year":"2011","unstructured":"Wang J-Z, Wang J-J, Zhang Z-G, Guo S-P (2011) Forecasting stock indices with back propagation neural network. Expert Syst Appl 38(11):14346\u201314355","journal-title":"Expert Syst Appl"},{"issue":"18","key":"9916_CR9","doi-asserted-by":"publisher","first-page":"18569","DOI":"10.1007\/s11042-016-4159-7","volume":"76","author":"R Singh","year":"2017","unstructured":"Singh R, Srivastava S (2017) Stock prediction using deep learning. Multimedia Tools Appl 76(18):18569\u201318584","journal-title":"Multimedia Tools Appl"},{"key":"9916_CR10","unstructured":"Ding X, Zhang Y, Liu T, Duan J (2015) Deep learning for event-driven stock prediction. In: Twenty-fourth international joint conference on artificial intelligence"},{"key":"9916_CR11","doi-asserted-by":"publisher","first-page":"1609","DOI":"10.1007\/s00521-019-04212-x","volume":"32","author":"P Yu","year":"2020","unstructured":"Yu P, Yan X (2020) Stock price prediction based on deep neural networks. Neural Comput Appl 32:1609\u20131628","journal-title":"Neural Comput Appl"},{"issue":"8","key":"9916_CR12","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735\u20131780","journal-title":"Neural Comput"},{"issue":"8","key":"9916_CR13","doi-asserted-by":"publisher","first-page":"2554","DOI":"10.1073\/pnas.79.8.2554","volume":"79","author":"JJ Hopfield","year":"1982","unstructured":"Hopfield JJ (1982) Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci 79(8):2554\u20132558","journal-title":"Proc Natl Acad Sci"},{"issue":"11","key":"9916_CR14","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278\u20132324","journal-title":"Proc IEEE"},{"issue":"9","key":"9916_CR15","doi-asserted-by":"publisher","first-page":"3942","DOI":"10.1109\/TNNLS.2020.3016291","volume":"32","author":"Q Ma","year":"2020","unstructured":"Ma Q, Li S, Zhuang W, Wang J, Zeng D (2020) Self-supervised time series clustering with model-based dynamics. IEEE Trans Neural Netw Learn Syst 32(9):3942\u20133955","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"9916_CR16","doi-asserted-by":"crossref","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), pp. 1643\u20131647. IEEE","DOI":"10.1109\/ICACCI.2017.8126078"},{"key":"9916_CR17","doi-asserted-by":"crossref","unstructured":"Chen K, Zhou Y, Dai F (2015) A lstm-based method for stock returns prediction: a case study of china stock market. In: 2015 IEEE International Conference on Big Data (big Data), pp. 2823\u20132824. IEEE","DOI":"10.1109\/BigData.2015.7364089"},{"issue":"1","key":"9916_CR18","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1049\/cit2.12059","volume":"8","author":"S Mukherjee","year":"2023","unstructured":"Mukherjee S, Sadhukhan B, Sarkar N, Roy D, De S (2023) Stock market prediction using deep learning algorithms. CAAI Trans Intell Technol 8(1):82\u201394","journal-title":"CAAI Trans Intell Technol"},{"key":"9916_CR19","doi-asserted-by":"crossref","unstructured":"Cho K, Van\u00a0Merri\u00ebnboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078","DOI":"10.3115\/v1\/D14-1179"},{"key":"9916_CR20","doi-asserted-by":"publisher","first-page":"895","DOI":"10.1016\/j.procs.2018.04.298","volume":"131","author":"G Shen","year":"2018","unstructured":"Shen G, Tan Q, Zhang H, Zeng P, Xu J (2018) Deep learning with gated recurrent unit networks for financial sequence predictions. Proced Comput Sci 131:895\u2013903","journal-title":"Proced Comput Sci"},{"issue":"13","key":"9916_CR21","doi-asserted-by":"publisher","first-page":"9713","DOI":"10.1007\/s00521-019-04504-2","volume":"32","author":"Z Jin","year":"2020","unstructured":"Jin Z, Yang Y, Liu Y (2020) Stock closing price prediction based on sentiment analysis and lstm. Neural Comput Appl 32(13):9713\u20139729","journal-title":"Neural Comput Appl"},{"key":"9916_CR22","doi-asserted-by":"crossref","unstructured":"Yoo PD, Kim MH, Jan T (2005) Machine learning techniques and use of event information for stock market prediction: A survey and evaluation. In: international conference on computational intelligence for modelling, control and automation and international conference on intelligent agents, web technologies and internet commerce (CIMCA-IAWTIC\u201906), vol. 2, pp. 835\u2013841. IEEE","DOI":"10.1109\/CIMCA.2005.1631572"},{"key":"9916_CR23","first-page":"17","volume":"30","author":"A Vaswani","year":"2017","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 Inform Process Syst 30:17","journal-title":"Adv Neural Inform Process Syst"},{"issue":"3","key":"9916_CR24","doi-asserted-by":"publisher","first-page":"1181","DOI":"10.1016\/j.ijforecast.2019.07.001","volume":"36","author":"D Salinas","year":"2020","unstructured":"Salinas D, Flunkert V, Gasthaus J, Januschowski T (2020) Deepar: probabilistic forecasting with autoregressive recurrent networks. Int J Forecast 36(3):1181\u20131191","journal-title":"Int J Forecast"},{"key":"9916_CR25","doi-asserted-by":"crossref","unstructured":"Liao Y, Liang C (2021) A temperature time series forecasting model based on deepar. In: 2021 7th international conference on computer and communications (ICCC), pp. 1588\u20131593. IEEE","DOI":"10.1109\/ICCC54389.2021.9674623"},{"issue":"3","key":"9916_CR26","doi-asserted-by":"publisher","first-page":"2814","DOI":"10.1109\/TII.2022.3160696","volume":"19","author":"P Arora","year":"2022","unstructured":"Arora P, Jalali SMJ, Ahmadian S, Panigrahi BK, Suganthan P, Khosravi A (2022) Probabilistic wind power forecasting using optimised deep auto-regressive recurrent neural networks. IEEE Trans Ind Inform 19(3):2814\u20132825","journal-title":"IEEE Trans Ind Inform"},{"issue":"7","key":"9916_CR27","doi-asserted-by":"publisher","first-page":"894","DOI":"10.3390\/atmos12070894","volume":"12","author":"F Jiang","year":"2021","unstructured":"Jiang F, Han X, Zhang W, Chen G (2021) Atmospheric pm2.5 prediction using deepar optimized by sparrow search algorithm with opposition-based and fitness-based learning. Atmosphere 12(7):894","journal-title":"Atmosphere"},{"key":"9916_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.117239","volume":"202","author":"Q Zhang","year":"2022","unstructured":"Zhang Q, Qin C, Zhang Y, Bao F, Zhang C, Liu P (2022) Transformer-based attention network for stock movement prediction. Expert Syst Appl 202:117239","journal-title":"Expert Syst Appl"},{"key":"9916_CR29","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1016\/j.neucom.2021.09.072","volume":"467","author":"H Xu","year":"2022","unstructured":"Xu H, Chai L, Luo Z, Li S (2022) Stock movement prediction via gated recurrent unit network based on reinforcement learning with incorporated attention mechanisms. Neurocomputing 467:214\u2013228","journal-title":"Neurocomputing"},{"key":"9916_CR30","doi-asserted-by":"crossref","unstructured":"Chen Y-J, Chen Y-M (2013) A fundamental analysis-based method for stock market forecasting, 354\u2013359","DOI":"10.1109\/ICICIP.2013.6568097"},{"issue":"4","key":"9916_CR31","doi-asserted-by":"publisher","first-page":"3007","DOI":"10.1007\/s10462-019-09754-z","volume":"53","author":"IK Nti","year":"2020","unstructured":"Nti IK, Adekoya AF, Weyori BA (2020) A systematic review of fundamental and technical analysis of stock market predictions. Artif Intell Rev 53(4):3007\u20133057","journal-title":"Artif Intell Rev"},{"key":"9916_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.116659","volume":"197","author":"MM Kumbure","year":"2022","unstructured":"Kumbure MM, Lohrmann C, Luukka P, Porras J (2022) Machine learning techniques and data for stock market forecasting: a literature review. Expert Syst Appl 197:116659","journal-title":"Expert Syst Appl"},{"issue":"2\u20133","key":"9916_CR33","doi-asserted-by":"publisher","first-page":"413","DOI":"10.1016\/0165-4101(92)90026-X","volume":"15","author":"AC Greig","year":"1992","unstructured":"Greig AC (1992) Fundamental analysis and subsequent stock returns. J Account Econom 15(2\u20133):413\u2013442","journal-title":"J Account Econom"},{"key":"9916_CR34","unstructured":"Mittal A, Goel A (2012) Stock prediction using twitter sentiment analysis. Standford University, CS229 (2011 http:\/\/cs229. stanford. edu\/proj2011\/GoelMittal-StockMarketPredictionUsingTwitterSentimentAnalysis. pdf) 15, 2352"},{"key":"9916_CR35","doi-asserted-by":"crossref","unstructured":"Makrehchi M, Shah S, Liao W (2013) Stock prediction using event-based sentiment analysis. In: 2013 IEEE\/WIC\/ACM international joint conferences on web intelligence (WI) and intelligent agent technologies (IAT), vol. 1, pp. 337\u2013342. IEEE","DOI":"10.1109\/WI-IAT.2013.48"},{"key":"9916_CR36","first-page":"1170","volume":"2014","author":"H Lee","year":"2014","unstructured":"Lee H, Surdeanu M, MacCartney B, Jurafsky D (2014) On the importance of text analysis for stock price prediction. LREC 2014:1170\u20131175","journal-title":"LREC"},{"issue":"24","key":"9916_CR37","doi-asserted-by":"publisher","first-page":"9603","DOI":"10.1016\/j.eswa.2015.07.052","volume":"42","author":"TH Nguyen","year":"2015","unstructured":"Nguyen TH, Shirai K, Velcin J (2015) Sentiment analysis on social media for stock movement prediction. Expert Syst Appl 42(24):9603\u20139611","journal-title":"Expert Syst Appl"},{"key":"9916_CR38","doi-asserted-by":"crossref","unstructured":"Akita R, Yoshihara A, Matsubara T, Uehara K (2016) Deep learning for stock prediction using numerical and textual information. In: 2016 IEEE\/ACIS 15th international conference on computer and information science (ICIS), pp. 1\u20136","DOI":"10.1109\/ICIS.2016.7550882"},{"issue":"1","key":"9916_CR39","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-017-0111-6","volume":"5","author":"S Sohangir","year":"2018","unstructured":"Sohangir S, Wang D, Pomeranets A, Khoshgoftaar TM (2018) Big data: deep learning for financial sentiment analysis. J Big Data 5(1):1\u201325","journal-title":"J Big Data"},{"key":"9916_CR40","doi-asserted-by":"crossref","unstructured":"Nourbakhsh Z, Habibi N (2022) Combining lstm and cnn methods and fundamental analysis for stock price trend prediction. Multim Tools Appl 1\u201331","DOI":"10.1007\/s11042-022-13963-0"},{"key":"9916_CR41","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1016\/j.eswa.2016.02.006","volume":"55","author":"RC Cavalcante","year":"2016","unstructured":"Cavalcante RC, Brasileiro RC, Souza VL, Nobrega JP, Oliveira AL (2016) Computational intelligence and financial markets: a survey and future directions. Expert Syst Appl 55:194\u2013211","journal-title":"Expert Syst Appl"},{"issue":"10","key":"9916_CR42","doi-asserted-by":"publisher","first-page":"6885","DOI":"10.1016\/j.eswa.2010.03.033","volume":"37","author":"LA Teixeira","year":"2010","unstructured":"Teixeira LA, De Oliveira ALI (2010) A method for automatic stock trading combining technical analysis and nearest neighbor classification. Expert Syst Appl 37(10):6885\u20136890","journal-title":"Expert Syst Appl"},{"key":"9916_CR43","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"},{"key":"9916_CR44","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115537","volume":"184","author":"W Jiang","year":"2021","unstructured":"Jiang W (2021) Applications of deep learning in stock market prediction: recent progress. Expert Syst Appl 184:115537","journal-title":"Expert Syst Appl"},{"key":"9916_CR45","doi-asserted-by":"crossref","unstructured":"Ariyo AA, Adewumi AO, Ayo CK (2014) Stock price prediction using the arima model. In: 2014 UKSim-AMSS 16th international conference on computer modelling and simulation, pp. 106\u2013112. IEEE","DOI":"10.1109\/UKSim.2014.67"},{"key":"9916_CR46","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1155\/2014\/614342","volume":"2014","author":"AA Adebiyi","year":"2014","unstructured":"Adebiyi AA, Adewumi AO, Ayo CK (2014) Comparison of arima and artificial neural networks models for stock price prediction. J Appl Math 2014:4","journal-title":"J Appl Math"},{"issue":"10","key":"9916_CR47","first-page":"9","volume":"15","author":"D-H Shin","year":"2017","unstructured":"Shin D-H, Choi K-H, Kim C-B (2017) Deep learning model for prediction rate improvement of stock price using rnn and lstm. J Korean Inst Inform Technol 15(10):9\u201316","journal-title":"J Korean Inst Inform Technol"},{"key":"9916_CR48","first-page":"18","volume":"31","author":"RT Chen","year":"2018","unstructured":"Chen RT, Rubanova Y, Bettencourt J, Duvenaud DK (2018) Neural ordinary differential equations. Adv Neural Inform Process Syst 31:18","journal-title":"Adv Neural Inform Process Syst"},{"key":"9916_CR49","doi-asserted-by":"crossref","unstructured":"Du S, Luo Y, Chen W, Xu J, Zeng D (2022) To-flow: Efficient continuous normalizing flows with temporal optimization adjoint with moving speed. In: proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 12570\u201312580","DOI":"10.1109\/CVPR52688.2022.01224"},{"issue":"6","key":"9916_CR50","doi-asserted-by":"publisher","first-page":"3461","DOI":"10.1007\/s00500-021-06113-5","volume":"27","author":"F Yang","year":"2023","unstructured":"Yang F, Chen J, Liu Y (2023) Improved and optimized recurrent neural network based on pso and its application in stock price prediction. Soft Comput 27(6):3461\u20133476","journal-title":"Soft Comput"},{"key":"9916_CR51","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.117370","volume":"202","author":"Y Chen","year":"2022","unstructured":"Chen Y, Wu J, Wu Z (2022) China\u2019s commercial bank stock price prediction using a novel k-means-lstm hybrid approach. Expert Syst Appl 202:117370","journal-title":"Expert Syst Appl"},{"key":"9916_CR52","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.117123","volume":"202","author":"A Kanwal","year":"2022","unstructured":"Kanwal A, Lau MF, Ng SP, Sim KY, Chandrasekaran S (2022) Bicudnnlstm-1dcnn-a hybrid deep learning-based predictive model for stock price prediction. Expert Syst Appl 202:117123","journal-title":"Expert Syst Appl"},{"key":"9916_CR53","unstructured":"Li H, Shen Y, Zhu Y (2018) Stock price prediction using attention-based multi-input lstm. In: Asian conference on machine learning, pp. 454\u2013469. PMLR"},{"key":"9916_CR54","doi-asserted-by":"publisher","first-page":"109759","DOI":"10.1016\/j.patcog.2023.109759","volume":"143","author":"C Cui","year":"2023","unstructured":"Cui C, Li X, Zhang C, Guan W, Wang M (2023) Temporal-relational hypergraph tri-attention networks for stock trend prediction. Patt Recogn 143:109759","journal-title":"Patt Recogn"},{"key":"9916_CR55","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"},{"key":"9916_CR56","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.110262","volume":"262","author":"J Wang","year":"2023","unstructured":"Wang J, Hu Y, Jiang T-X, Tan J, Li Q (2023) Essential tensor learning for multimodal information-driven stock movement prediction. Knowl Based Syst 262:110262","journal-title":"Knowl Based Syst"},{"key":"9916_CR57","doi-asserted-by":"crossref","unstructured":"Pei W, Baltrusaitis T, Tax DM, Morency L-P (2017) Temporal attention-gated model for robust sequence classification. In: proceedings of the IEEE conference on computer vision and pattern recognition, pp. 6730\u20136739","DOI":"10.1109\/CVPR.2017.94"},{"issue":"20","key":"9916_CR58","doi-asserted-by":"publisher","first-page":"17193","DOI":"10.1007\/s00521-022-07687-3","volume":"34","author":"A Rostamian","year":"2022","unstructured":"Rostamian A, O\u2019Hara JG (2022) Event prediction within directional change framework using a cnn-lstm model. Neural Comput Appl 34(20):17193\u201317205","journal-title":"Neural Comput Appl"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-09916-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-09916-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-09916-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T15:25:55Z","timestamp":1724426755000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-09916-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,15]]},"references-count":58,"alternative-id":["9916"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-09916-3","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,15]]},"assertion":[{"value":"30 November 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 April 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 May 2024","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 there is no conflict of interest regarding the publication of this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Our study followed ethical guidelines and obtained informed consent from all participants regarding the data used.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}