{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T17:51:03Z","timestamp":1782582663209,"version":"3.54.5"},"reference-count":77,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100007839","name":"Zhongnan University of Economics and Law","doi-asserted-by":"publisher","award":["14303622"],"award-info":[{"award-number":["14303622"]}],"id":[{"id":"10.13039\/100007839","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007839","name":"Zhongnan University of Economics and Law","doi-asserted-by":"publisher","award":["2722022BY020"],"award-info":[{"award-number":["2722022BY020"]}],"id":[{"id":"10.13039\/100007839","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007839","name":"Zhongnan University of Economics and Law","doi-asserted-by":"publisher","award":["2722024EJ011"],"award-info":[{"award-number":["2722024EJ011"]}],"id":[{"id":"10.13039\/100007839","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002920","name":"Research Grants Council, University Grants Committee","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002920","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004853","name":"The Chinese University of Hong Kong","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004853","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Engineering Applications of Artificial Intelligence"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.engappai.2026.114755","type":"journal-article","created":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T03:01:17Z","timestamp":1775530877000},"page":"114755","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"special_numbering":"P1","title":["Leveraging high-frequency intraday pattern for improved deep temporal learning of stock price dynamics"],"prefix":"10.1016","volume":"176","author":[{"given":"Jiaqi","family":"He","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-4005-5116","authenticated-orcid":false,"given":"Yifan","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyuan","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4550-2285","authenticated-orcid":false,"given":"Hanwen","family":"Ning","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.engappai.2026.114755_b1","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1214\/07-AOS568","article-title":"Testing for jumps in a discretely observed process","volume":"37","author":"A\u00eft-Sahalia","year":"2009","journal-title":"Ann. Statist."},{"issue":"2","key":"10.1016\/j.engappai.2026.114755_b2","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1016\/j.jeconom.2017.08.015","article-title":"Using principal component analysis to estimate a high dimensional factor model with high-frequency data","volume":"201","author":"A\u00eft-Sahalia","year":"2017","journal-title":"J. Econometrics"},{"issue":"525","key":"10.1016\/j.engappai.2026.114755_b3","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1080\/01621459.2017.1401542","article-title":"Principal component analysis of high-frequency data","volume":"114","author":"A\u00eft-Sahalia","year":"2019","journal-title":"J. Amer. Statist. Assoc."},{"key":"10.1016\/j.engappai.2026.114755_b4","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2023.110139","article-title":"Hybrid ARMA-GARCH-neural networks for intraday strategy exploration in high-frequency trading","volume":"148","author":"Alaminos","year":"2024","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.engappai.2026.114755_b5","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.106106","article-title":"Intelligent forecasting model of stock price using neighborhood rough set and multivariate empirical mode decomposition","volume":"122","author":"Bai","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.114755_b6","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.106779","article-title":"Assessing project portfolio risk via an enhanced GA-BPNN combined with PCA","volume":"126","author":"Bai","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"2","key":"10.1016\/j.engappai.2026.114755_b7","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1080\/14697688.2013.867454","article-title":"Ensemble properties of high-frequency data and intraday trading rules","volume":"15","author":"Baldovin","year":"2015","journal-title":"Quant. Finance"},{"key":"10.1016\/j.engappai.2026.114755_b8","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.ins.2017.04.013","article-title":"Discovering profitable stocks for intraday trading","volume":"405","author":"Baralis","year":"2017","journal-title":"Inform. Sci."},{"issue":"2","key":"10.1016\/j.engappai.2026.114755_b9","first-page":"271","article-title":"Measuring the frequency dynamics of financial connectedness and systemic risk","volume":"16","author":"Barun\u00edk","year":"2018","journal-title":"J. Finance Econ."},{"key":"10.1016\/j.engappai.2026.114755_b10","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.107617","article-title":"Forecasting multistep daily stock prices for long-term investment decisions: A study of deep learning models on global indices","volume":"129","author":"Beniwal","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"1","key":"10.1016\/j.engappai.2026.114755_b11","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1111\/j.1540-6261.1994.tb04424.x","article-title":"Market statistics and technical analysis: The role of volume","volume":"49","author":"Blume","year":"1994","journal-title":"J. Finance"},{"key":"10.1016\/j.engappai.2026.114755_b12","article-title":"On the intraday return curves of bitcoin: Predictability and trading opportunities","volume":"76","author":"Bouri","year":"2021","journal-title":"Int. Rev. Finance Anal."},{"key":"10.1016\/j.engappai.2026.114755_b13","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2022.109213","article-title":"Joint classification and prediction of random curves using heavy-tailed process functional regression","volume":"136","author":"Cao","year":"2023","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.engappai.2026.114755_b14","doi-asserted-by":"crossref","DOI":"10.1016\/j.ins.2024.121244","article-title":"An improved deep temporal convolutional network for new energy stock index prediction","volume":"682","author":"Chen","year":"2024","journal-title":"Inform. Sci."},{"key":"10.1016\/j.engappai.2026.114755_b15","first-page":"135","article-title":"A subordinated stochastic process model with finite variance for speculative prices","author":"Clark","year":"1973","journal-title":"Econ.: J. Econ. Soc."},{"issue":"10","key":"10.1016\/j.engappai.2026.114755_b16","doi-asserted-by":"crossref","first-page":"2035","DOI":"10.1016\/S0378-4266(02)00321-7","article-title":"Intraday trading volume and return volatility of the DJIA stocks: A note","volume":"27","author":"Darrat","year":"2003","journal-title":"J. Bank. Finance"},{"key":"10.1016\/j.engappai.2026.114755_b17","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2023.109440","article-title":"Multi-stage stacked temporal convolution neural networks (MS-S-TCNs) for biosignal segmentation and anomaly localization","volume":"139","author":"Dissanayake","year":"2023","journal-title":"Pattern Recognit."},{"issue":"4","key":"10.1016\/j.engappai.2026.114755_b18","first-page":"1606","article-title":"What is the optimal trading frequency in financial markets?","volume":"84","author":"Du","year":"2017","journal-title":"Rev. Econ. Stud."},{"key":"10.1016\/j.engappai.2026.114755_b19","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2022.109945","article-title":"Temporal convolutional networks with RNN approach for chaotic time series prediction","volume":"133","author":"Dudukcu","year":"2023","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.engappai.2026.114755_b20","series-title":"GARCH Models: Structure, Statistical Inference and Financial Applications","author":"Francq","year":"2019"},{"issue":"2","key":"10.1016\/j.engappai.2026.114755_b21","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1016\/j.jfineco.2018.05.009","article-title":"Market intraday momentum","volume":"129","author":"Gao","year":"2018","journal-title":"J. Finance Econ."},{"issue":"1","key":"10.1016\/j.engappai.2026.114755_b22","article-title":"Stock prediction based on optimized LSTM and GRU models","volume":"2021","author":"Gao","year":"2021","journal-title":"Sci. Program."},{"key":"10.1016\/j.engappai.2026.114755_b23","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.iref.2017.01.020","article-title":"Forecasting stock index futures returns with mixed-frequency sentiment","volume":"49","author":"Gao","year":"2017","journal-title":"Int. Rev. Econ. Finance"},{"issue":"3","key":"10.1016\/j.engappai.2026.114755_b24","doi-asserted-by":"crossref","first-page":"877","DOI":"10.1111\/0022-1082.00349","article-title":"The high-volume return premium","volume":"56","author":"Gervais","year":"2001","journal-title":"J. Finance"},{"issue":"1","key":"10.1016\/j.engappai.2026.114755_b25","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/0304-405X(85)90044-3","article-title":"Bid, ask and transaction prices in a specialist market with heterogeneously informed traders","volume":"14","author":"Glosten","year":"1985","journal-title":"J. Finance Econ."},{"issue":"5","key":"10.1016\/j.engappai.2026.114755_b26","doi-asserted-by":"crossref","first-page":"2223","DOI":"10.1093\/rfs\/hhaa009","article-title":"Empirical asset pricing via machine learning","volume":"33","author":"Gu","year":"2020","journal-title":"Rev. Finance Stud."},{"key":"10.1016\/j.engappai.2026.114755_b27","series-title":"Econometrics of Financial High-Frequency Data","author":"Hautsch","year":"2011"},{"issue":"2","key":"10.1016\/j.engappai.2026.114755_b28","first-page":"485","article-title":"Intraday market predictability: A machine learning approach","volume":"21","author":"Huddleston","year":"2023","journal-title":"J. Finance Econ."},{"key":"10.1016\/j.engappai.2026.114755_b29","doi-asserted-by":"crossref","DOI":"10.1016\/j.irfa.2023.102790","article-title":"Applications of high-frequency data in finance: A bibliometric literature review","author":"Hussain","year":"2023","journal-title":"Int. Rev. Finance Anal."},{"key":"10.1016\/j.engappai.2026.114755_b30","article-title":"Betting against beta with intraday and overnight signals","volume":"86","author":"Insana","year":"2023","journal-title":"Int. Rev. Finance Anal."},{"issue":"7","key":"10.1016\/j.engappai.2026.114755_b31","doi-asserted-by":"crossref","first-page":"2249","DOI":"10.1016\/j.spa.2008.11.004","article-title":"Microstructure noise in the continuous case: the pre-averaging approach","volume":"119","author":"Jacod","year":"2009","journal-title":"Stochastic Process. Appl."},{"issue":"8","key":"10.1016\/j.engappai.2026.114755_b32","doi-asserted-by":"crossref","first-page":"2910","DOI":"10.1016\/j.spa.2015.02.005","article-title":"Microstructure noise in the continuous case: Approximate efficiency of the adaptive pre-averaging method","volume":"125","author":"Jacod","year":"2015","journal-title":"Stochastic Process. Appl."},{"issue":"6","key":"10.1016\/j.engappai.2026.114755_b33","doi-asserted-by":"crossref","first-page":"3193","DOI":"10.1111\/jofi.13268","article-title":"(Re-) Imag (in) ing price trends","volume":"78","author":"Jiang","year":"2023","journal-title":"J. Finance"},{"key":"10.1016\/j.engappai.2026.114755_b34","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2024.109702","article-title":"Informer learning framework based on secondary decomposition for multi-step forecast of ultra-short term wind speed","volume":"139","author":"Jin","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.114755_b35","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2022.104713","article-title":"Intelligent fault diagnosis of train axle box bearing based on parameter optimization VMD and improved DBN","volume":"110","author":"Jin","year":"2022","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"5","key":"10.1016\/j.engappai.2026.114755_b36","doi-asserted-by":"crossref","first-page":"1546","DOI":"10.1016\/j.spa.2012.12.011","article-title":"Asymptotic normality of the principal components of functional time series","volume":"123","author":"Kokoszka","year":"2013","journal-title":"Stochastic Process. Appl."},{"issue":"3","key":"10.1016\/j.engappai.2026.114755_b37","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1111\/ectj.12006","article-title":"Predictability of shapes of intraday price curves","volume":"16","author":"Kokoszka","year":"2013","journal-title":"Econom. J."},{"key":"10.1016\/j.engappai.2026.114755_b38","first-page":"1315","article-title":"Continuous auctions and insider trading","author":"Kyle","year":"1985","journal-title":"Econ.: J. Econ. Soc."},{"issue":"6","key":"10.1016\/j.engappai.2026.114755_b39","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3136625","article-title":"Feature selection: A data perspective","volume":"50","author":"Li","year":"2017","journal-title":"ACM Comput. Surv."},{"issue":"2","key":"10.1016\/j.engappai.2026.114755_b40","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1080\/15427560.2017.1376669","article-title":"How does high-frequency trading affect low-frequency trading?","volume":"19","author":"Li","year":"2018","journal-title":"J. Behav. Finance"},{"key":"10.1016\/j.engappai.2026.114755_b41","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.107227","article-title":"Prediction of the transient emission characteristics from diesel engine using temporal convolutional networks","volume":"127","author":"Liao","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.114755_b42","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2021.108096","article-title":"Principal component analysis in the wavelet domain","volume":"119","author":"Lim","year":"2021","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.engappai.2026.114755_b43","series-title":"Itransformer: Inverted transformers are effective for time series forecasting","author":"Liu","year":"2023"},{"key":"10.1016\/j.engappai.2026.114755_b44","article-title":"A stock series prediction model based on variational mode decomposition and dual-channel attention network","volume":"238","author":"Liu","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.engappai.2026.114755_b45","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.126803","article-title":"Stockformer: A price\u2013volume factor stock selection model based on wavelet transform and multi-task self-attention networks","volume":"273","author":"Ma","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.engappai.2026.114755_b46","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.106705","article-title":"A novel feature engineering approach for high-frequency financial data","volume":"125","author":"Mantilla","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"2","key":"10.1016\/j.engappai.2026.114755_b47","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1016\/j.jfineco.2011.11.003","article-title":"Time series momentum","volume":"104","author":"Moskowitz","year":"2012","journal-title":"J. Finance Econ."},{"key":"10.1016\/j.engappai.2026.114755_b48","series-title":"A time series is worth 64words: Long-term forecasting with transformers","author":"Nie","year":"2022"},{"issue":"2","key":"10.1016\/j.engappai.2026.114755_b49","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1088\/1469-7688\/1\/2\/308","article-title":"Price fluctuations, market activity and trading volume","volume":"1","author":"Plerou","year":"2001","journal-title":"Quant. Finance"},{"issue":"52","key":"10.1016\/j.engappai.2026.114755_b50","doi-asserted-by":"crossref","first-page":"22079","DOI":"10.1073\/pnas.0911983106","article-title":"Cross-correlations between volume change and price change","volume":"106","author":"Podobnik","year":"2009","journal-title":"Proc. Natl. Acad. Sci."},{"issue":"3","key":"10.1016\/j.engappai.2026.114755_b51","first-page":"472","article-title":"Intraday end-of-day volume prediction","volume":"19","author":"Sancetta","year":"2021","journal-title":"J. Finance Econ."},{"issue":"3","key":"10.1016\/j.engappai.2026.114755_b52","doi-asserted-by":"crossref","first-page":"643","DOI":"10.1016\/j.ejor.2014.01.019","article-title":"Forecasting the volatility of crude oil futures using intraday data","volume":"235","author":"S\u00e9vi","year":"2014","journal-title":"European J. Oper. Res."},{"issue":"7","key":"10.1016\/j.engappai.2026.114755_b53","doi-asserted-by":"crossref","first-page":"741","DOI":"10.1002\/for.2467","article-title":"Forecasting intraday S&P 500 index returns: A functional time series approach","volume":"36","author":"Shang","year":"2017","journal-title":"J. Forecast."},{"issue":"1\u20132","key":"10.1016\/j.engappai.2026.114755_b54","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1007\/s10479-018-3108-4","article-title":"Intraday forecasts of a volatility index: Functional time series methods with dynamic updating","volume":"282","author":"Shang","year":"2019","journal-title":"Ann. Oper. Res."},{"key":"10.1016\/j.engappai.2026.114755_b55","article-title":"Stock price trend forecasting based on multi-channel complementary network with CEEMDAN decomposition and transformer residual prediction","author":"Shen","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.engappai.2026.114755_b56","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2024.108638","article-title":"A dual-time dual-population multi-objective evolutionary algorithm with application to the portfolio optimization problem","volume":"133","author":"Song","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.114755_b57","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.neucom.2017.02.097","article-title":"Stock portfolio selection using learning-to-rank algorithms with news sentiment","volume":"264","author":"Song","year":"2017","journal-title":"Neurocomputing"},{"key":"10.1016\/j.engappai.2026.114755_b58","doi-asserted-by":"crossref","DOI":"10.1016\/j.ribaf.2022.101625","article-title":"The intraday dynamics and intraday price discovery of bitcoin","volume":"60","author":"Su","year":"2022","journal-title":"Res. Int. Bus. Finance"},{"key":"10.1016\/j.engappai.2026.114755_b59","first-page":"485","article-title":"The price variability-volume relationship on speculative markets","author":"Tauchen","year":"1983","journal-title":"Econ.: J. Econ. Soc."},{"key":"10.1016\/j.engappai.2026.114755_b60","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2021.114800","article-title":"A comprehensive survey on deep neural networks for stock market: The need, challenges, and future directions","volume":"177","author":"Thakkar","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.engappai.2026.114755_b61","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.114263","article-title":"BiMT-TCN: A cutting-edge hybrid model for enhanced stock price prediction","author":"Tian","year":"2025","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.engappai.2026.114755_b62","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.124088","article-title":"MultiWaveNet: A long time series forecasting framework based on multi-scale analysis and multi-channel feature fusion","volume":"251","author":"Tian","year":"2024","journal-title":"Expert Syst. Appl."},{"issue":"2","key":"10.1016\/j.engappai.2026.114755_b63","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2591672","article-title":"Stock prediction by searching for similarities in candlestick charts","volume":"5","author":"Tsai","year":"2014","journal-title":"ACM Trans. Manag. Inf. Syst. (TMIS)"},{"key":"10.1016\/j.engappai.2026.114755_b64","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.engappai.2026.114755_b65","doi-asserted-by":"crossref","DOI":"10.1016\/j.rse.2025.114841","article-title":"A TCN-transformer parallel model for reconstruction of a global, daily, spatially seamless FY-3B soil moisture dataset","volume":"328","author":"Wang","year":"2025","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.engappai.2026.114755_b66","doi-asserted-by":"crossref","DOI":"10.1016\/j.najef.2022.101733","article-title":"Intraday return predictability in the cryptocurrency markets: Momentum, reversal, or both","volume":"62","author":"Wen","year":"2022","journal-title":"North Am. J. Econ. Finance"},{"issue":"1","key":"10.1016\/j.engappai.2026.114755_b67","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/j.ejor.2023.12.012","article-title":"Variance swaps with mean reversion and multi-factor variance","volume":"315","author":"Wu","year":"2024","journal-title":"European J. Oper. Res."},{"issue":"5","key":"10.1016\/j.engappai.2026.114755_b68","doi-asserted-by":"crossref","first-page":"4189","DOI":"10.1007\/s10489-024-05377-2","article-title":"Momentum portfolio selection based on learning-to-rank algorithms with heterogeneous knowledge graphs","volume":"54","author":"Wu","year":"2024","journal-title":"Appl. Intell."},{"key":"10.1016\/j.engappai.2026.114755_b69","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2023.109872","article-title":"Online portfolio selection with predictive instantaneous risk assessment","volume":"144","author":"Xi","year":"2023","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.engappai.2026.114755_b70","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2024.108320","article-title":"A hierarchical deep model integrating economic facts for stock movement prediction","volume":"133","author":"Yang","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.114755_b71","doi-asserted-by":"crossref","DOI":"10.1016\/j.est.2025.119417","article-title":"A temporal convolutional network-transformer-CrossAttention model for state of charge estimation of lithium-ion batteries","volume":"141","author":"Yang","year":"2026","journal-title":"J. Energy Storage"},{"key":"10.1016\/j.engappai.2026.114755_b72","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2023.110356","article-title":"Stock index forecasting based on multivariate empirical mode decomposition and temporal convolutional networks","volume":"142","author":"Yao","year":"2023","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.engappai.2026.114755_b73","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.111849","article-title":"DTAAD: Dual TCN-attention networks for anomaly detection in multivariate time series data","volume":"295","author":"Yu","year":"2024","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.engappai.2026.114755_b74","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2024.109005","article-title":"Short-term high-speed rail passenger flow prediction by integrating ensemble empirical mode decomposition with multivariate grey support vector machine","volume":"136","author":"Yuan","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.114755_b75","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.117951","article-title":"Forecasting stock volatility and value-at-risk based on temporal convolutional networks","volume":"207","author":"Zhang","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.engappai.2026.114755_b76","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2024.110985","article-title":"Dynamic convolutional time series forecasting based on adaptive temporal bilateral filtering","volume":"158","author":"Zhang","year":"2025","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.engappai.2026.114755_b77","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2022.108543","article-title":"Auto uning of price prediction models for high-frequency trading via reinforcement learning","volume":"125","author":"Zhang","year":"2022","journal-title":"Pattern Recognit."}],"container-title":["Engineering Applications of Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626010377?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626010377?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T22:42:52Z","timestamp":1778625772000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0952197626010377"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":77,"alternative-id":["S0952197626010377"],"URL":"https:\/\/doi.org\/10.1016\/j.engappai.2026.114755","relation":{},"ISSN":["0952-1976"],"issn-type":[{"value":"0952-1976","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Leveraging high-frequency intraday pattern for improved deep temporal learning of stock price dynamics","name":"articletitle","label":"Article Title"},{"value":"Engineering Applications of Artificial Intelligence","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.engappai.2026.114755","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"114755"}}