{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T04:15:09Z","timestamp":1782447309449,"version":"3.54.5"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"23","license":[{"start":{"date-parts":[[2023,10,2]],"date-time":"2023-10-02T00:00:00Z","timestamp":1696204800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,10,2]],"date-time":"2023-10-02T00:00:00Z","timestamp":1696204800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62173317"],"award-info":[{"award-number":["62173317"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Research and Development Program of Anhui","award":["202104a05020064"],"award-info":[{"award-number":["202104a05020064"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2023,12]]},"DOI":"10.1007\/s10489-023-04980-z","type":"journal-article","created":{"date-parts":[[2023,10,2]],"date-time":"2023-10-02T07:01:40Z","timestamp":1696230100000},"page":"28401-28417","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["TCLN: A Transformer-based Conv-LSTM network for multivariate time series forecasting"],"prefix":"10.1007","volume":"53","author":[{"given":"Shusen","family":"Ma","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianhao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3684-5297","authenticated-orcid":false,"given":"Yun-Bo","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Kang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"Bai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,10,2]]},"reference":[{"key":"4980_CR1","doi-asserted-by":"crossref","unstructured":"Prakhar K, Sountharrajan S, Suganya E, Karthiga M, Kumar S (2022) Effective stock price prediction using time series forecasting. In: 6th International Conference on Trends in Electronics and Informatics (ICOEI) pp 1636\u20131640","DOI":"10.1109\/ICOEI53556.2022.9776830"},{"key":"4980_CR2","doi-asserted-by":"publisher","unstructured":"Venkatachalam K, Trojovsk\u00fd P, Pamucar D, Bacanin N, Simic V (2023) DWFH: An improved data-driven deep weather forecasting hybrid model using transductive long short term memory (T-LSTM). Expert Syst Appl 213 (Part), 119270. https:\/\/doi.org\/10.1016\/j.eswa.2022.119270","DOI":"10.1016\/j.eswa.2022.119270"},{"key":"4980_CR3","doi-asserted-by":"publisher","unstructured":"Guo S, Lin Y, Feng N, Song C, Wan H (2019) Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. In: The thirty-third AAAI conference on artificial intelligence, pp 922\u2013929. https:\/\/doi.org\/10.1609\/aaai.v33i01.3301922","DOI":"10.1609\/aaai.v33i01.3301922"},{"issue":"7","key":"4980_CR4","doi-asserted-by":"publisher","DOI":"10.1007\/s11432-020-3071-8","volume":"64","author":"H Gao","year":"2021","unstructured":"Gao H, Su H, Cai Y, Wu R, Hao Z, Xu Y, Wu W, Wang J, Li Z, Kan Z (2021) Trajectory prediction of cyclist based on dynamic bayesian network and long short-term memory model at unsignalized intersections. Science China Information Sciences 64(7):172207. https:\/\/doi.org\/10.1007\/s11432-020-3071-8","journal-title":"Science China Information Sciences"},{"issue":"4","key":"4980_CR5","doi-asserted-by":"publisher","DOI":"10.1007\/s11432-018-9806-7","volume":"64","author":"H Shi","year":"2021","unstructured":"Shi H, Zhu J, Kuang M, Yuan X (2021) Cooperative prediction guidance law in target-attacker-defender scenario. Sci China Inf Sci 64(4):149201. https:\/\/doi.org\/10.1007\/s11432-018-9806-7","journal-title":"Sci China Inf Sci"},{"issue":"1","key":"4980_CR6","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1016\/j.ijforecast.2021.11.012","volume":"39","author":"D Gefang","year":"2023","unstructured":"Gefang D, Koop G, Poon A (2023) Forecasting using variational Bayesian inference in large vector autoregressions with hierarchical shrinkage. Int J Forecast 39(1):346\u2013363","journal-title":"Int J Forecast"},{"issue":"4","key":"4980_CR7","doi-asserted-by":"publisher","first-page":"1318","DOI":"10.1016\/j.ijforecast.2020.01.004","volume":"36","author":"B Zhang","year":"2020","unstructured":"Zhang B, Chan JCC, Cross JL (2020) Stochastic volatility models with ARMA innovations: An application to G7 inflation forecasts. Int J Forecast 36(4):1318\u20131328","journal-title":"Int J Forecast"},{"key":"4980_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2023.127069","volume":"272","author":"H Khajavi","year":"2023","unstructured":"Khajavi H, Rastgoo A (2023) Improving the prediction of heating energy consumed at residential buildings using a combination of support vector regression and meta-heuristic algorithms. Energy 272:127069. https:\/\/doi.org\/10.1016\/j.energy.2023.127069","journal-title":"Energy"},{"issue":"12","key":"4980_CR9","doi-asserted-by":"publisher","first-page":"13675","DOI":"10.1007\/s10489-022-03175-2","volume":"52","author":"T Swathi","year":"2022","unstructured":"Swathi T, Kasiviswanath N, Rao AA (2022) An optimal deep learning-based LSTM for stock price prediction using twitter sentiment analysis. Appl Intell 52(12):13675\u201313688. https:\/\/doi.org\/10.1007\/s10489-022-03175-2","journal-title":"Appl Intell"},{"issue":"2","key":"4980_CR10","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1109\/72.279181","volume":"5","author":"Y Bengio","year":"1994","unstructured":"Bengio Y, Simard PY, Frasconi P (1994) Learning long-term dependencies with gradient descent is difficult. IEEE Trans Neural Netw 5(2):157\u2013166. https:\/\/doi.org\/10.1109\/72.279181","journal-title":"IEEE Trans Neural Netw"},{"issue":"5","key":"4980_CR11","doi-asserted-by":"publisher","first-page":"2036","DOI":"10.1002\/int.22370","volume":"36","author":"Y Xiao","year":"2021","unstructured":"Xiao Y, Yin H, Zhang Y, Qi H, Zhang Y, Liu Z (2021) A dual-stage attention-based Conv-LSTM network for spatio-temporal correlation and multivariate time series prediction. Int J Intell Syst 36(5):2036\u20132057. https:\/\/doi.org\/10.1002\/int.22370","journal-title":"Int J Intell Syst"},{"key":"4980_CR12","doi-asserted-by":"publisher","unstructured":"Qin Y, Song D, Chen H, Cheng W, Jiang G, Cottrell GW (2017) A dual-stage attention-based recurrent neural network for time series prediction. In: IJCAI, pp 2627\u20132633. https:\/\/doi.org\/10.24963\/ijcai.2017\/366","DOI":"10.24963\/ijcai.2017\/366"},{"key":"4980_CR13","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1016\/j.neucom.2022.06.014","volume":"501","author":"E Fu","year":"2022","unstructured":"Fu E, Zhang Y, Yang F, Wang S (2022) Temporal self-attention-based Conv-LSTM network for multivariate time series prediction. Neurocomput 501:162\u2013173. https:\/\/doi.org\/10.1016\/j.neucom.2022.06.014","journal-title":"Neurocomput"},{"key":"4980_CR14","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 Proc Syst 30"},{"key":"4980_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2023.127678","volume":"278","author":"EGS Nascimento","year":"2023","unstructured":"Nascimento EGS, de Melo TAC, Moreira DM (2023) A transformer-based deep neural network with wavelet transform for forecasting wind speed and wind energy. Energy 278:127678. https:\/\/doi.org\/10.1016\/j.energy.2023.127678","journal-title":"Energy"},{"key":"4980_CR16","doi-asserted-by":"publisher","unstructured":"Zerveas G, Jayaraman S, Patel D, Bhamidipaty A, Eickhoff C (2021) A transformer-based framework for multivariate time series representation learning. In: KDD \u201921: The 27th ACM SIGKDD conference on knowledge discovery and data mining pp 2114\u20132124. https:\/\/doi.org\/10.1145\/3447548.3467401","DOI":"10.1145\/3447548.3467401"},{"issue":"4","key":"4980_CR17","doi-asserted-by":"publisher","first-page":"2904","DOI":"10.1109\/TSG.2020.2974021","volume":"11","author":"X Fu","year":"2020","unstructured":"Fu X, Guo Q, Sun H (2020) Statistical machine learning model for stochastic optimal planning of distribution networks considering a dynamic correlation and dimension reduction. IEEE Transactions on Smart Grid 11(4):2904\u20132917. https:\/\/doi.org\/10.1109\/TSG.2020.2974021","journal-title":"IEEE Transactions on Smart Grid"},{"issue":"1","key":"4980_CR18","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1186\/s41601-022-00228-z","volume":"7","author":"X Fu","year":"2022","unstructured":"Fu X (2022) Statistical machine learning model for capacitor planning considering uncertainties in photovoltaic power. Protect Contr Mod Power Syst 7(1):5. https:\/\/doi.org\/10.1186\/s41601-022-00228-z","journal-title":"Protect Contr Mod Power Syst"},{"key":"4980_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.irfa.2023.102627","volume":"87","author":"S Pan","year":"2023","unstructured":"Pan S, Long S, Wang Y, Xie Y (2023) Nonlinear asset pricing in Chinese stock market: A deep learning approach. Int Rev Fin Anal 87:102627. https:\/\/doi.org\/10.1016\/j.irfa.2023.102627","journal-title":"Int Rev Fin Anal"},{"issue":"12","key":"4980_CR20","doi-asserted-by":"publisher","first-page":"8784","DOI":"10.1007\/s10489-021-02359-6","volume":"51","author":"L Mohimont","year":"2021","unstructured":"Mohimont L, Chemchem A, Alin F, Krajecki M, Steffenel LA (2021) Convolutional neural networks and temporal CNNs for COVID-19 forecasting in France. Appl Intell 51(12):8784\u20138809. https:\/\/doi.org\/10.1007\/s10489-021-02359-6","journal-title":"Appl Intell"},{"issue":"8","key":"4980_CR21","doi-asserted-by":"publisher","first-page":"9117","DOI":"10.1007\/s10489-021-02845-x","volume":"52","author":"T Banerjee","year":"2022","unstructured":"Banerjee T, Sinha S, Choudhury P (2022) Long term and short term forecasting of horticultural produce based on the\u00a0LSTM network model. Appl Intell 52(8):9117\u20139147. https:\/\/doi.org\/10.1007\/s10489-021-02845-x","journal-title":"Appl Intell"},{"key":"4980_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2022.105717","volume":"119","author":"G Li","year":"2023","unstructured":"Li G, Zhong X (2023) Parking demand forecasting based on improved complete ensemble empirical mode decomposition and GRU model. Eng Appl Artif Intell 119:105717. https:\/\/doi.org\/10.1016\/j.engappai.2022.105717","journal-title":"Eng Appl Artif Intell"},{"issue":"8","key":"4980_CR23","doi-asserted-by":"publisher","first-page":"3002","DOI":"10.1007\/s10489-019-01426-3","volume":"49","author":"W Xu","year":"2019","unstructured":"Xu W, Peng H, Zeng X, Zhou F, Tian X, Peng X (2019) A hybrid modelling method for time series forecasting based on a linear regression model and deep learning. Appl Intell 49(8):3002\u20133015. https:\/\/doi.org\/10.1007\/s10489-019-01426-3","journal-title":"Appl Intell"},{"key":"4980_CR24","doi-asserted-by":"publisher","unstructured":"Lai G, Chang W, Yang Y, Liu H (2018) Modeling long- and short-term temporal patterns with deep neural networks. In: The 41st international ACM SIGIR conference on research & development in information retrieval pp 95\u2013104. https:\/\/doi.org\/10.1145\/3209978.3210006","DOI":"10.1145\/3209978.3210006"},{"key":"4980_CR25","doi-asserted-by":"crossref","unstructured":"Yang Y, Lu J (2022) Foreformer: an enhanced transformer-based framework for multivariate time series forecasting. Appl Intell 1\u201320","DOI":"10.1007\/s10489-022-04100-3"},{"issue":"12","key":"4980_CR26","doi-asserted-by":"publisher","first-page":"9179","DOI":"10.1109\/JIOT.2021.3100509","volume":"9","author":"Z Chen","year":"2022","unstructured":"Chen Z, Chen D, Zhang X, Yuan Z, Cheng X (2022) Learning graph structures with transformer for multivariate time-series anomaly detection in IoT. IEEE internet things J 9(12):9179\u20139189. https:\/\/doi.org\/10.1109\/JIOT.2021.3100509","journal-title":"IEEE internet things J"},{"key":"4980_CR27","first-page":"17766","volume":"33","author":"D Cao","year":"2020","unstructured":"Cao D, Wang Y, Duan J, Zhang C, Zhu X, Huang C, Tong Y, Xu B, Bai J, Tong J et al (2020) Spectral temporal graph neural network for multivariate time-series forecasting. Adv Neural Inf Proc Sys 33:17766\u201317778","journal-title":"Adv Neural Inf Proc Sys"},{"key":"4980_CR28","unstructured":"Shang C, Chen J, Bi J (2021) Discrete graph structure learning for forecasting multiple time series. In: 9th international conference on learning representations. https:\/\/openreview.net\/forum?id=WEHSlH5mOk"},{"key":"4980_CR29","doi-asserted-by":"publisher","unstructured":"Wu Z, Pan S, Long G, Jiang J, Chang X, Zhang C (2020) Connecting the dots: multivariate time series forecasting with graph neural networks. In: KDD \u201920: The 26th ACM SIGKDD conference on knowledge discovery and data mining pp 753\u2013763. https:\/\/doi.org\/10.1145\/3394486.3403118","DOI":"10.1145\/3394486.3403118"},{"issue":"1","key":"4980_CR30","doi-asserted-by":"publisher","first-page":"642","DOI":"10.1109\/TSTE.2022.3223684","volume":"14","author":"X Fu","year":"2023","unstructured":"Fu X, Zhou Y (2023) Collaborative optimization of PV greenhouses and clean energy systems in rural areas. IEEE transactions on sustainable energy 14(1):642\u2013656. https:\/\/doi.org\/10.1109\/TSTE.2022.3223684","journal-title":"IEEE transactions on sustainable energy"},{"issue":"15","key":"4980_CR31","doi-asserted-by":"publisher","first-page":"17371","DOI":"10.1007\/s10489-022-03324-7","volume":"52","author":"X Huang","year":"2022","unstructured":"Huang X, Tang J, Yang X, Xiong L (2022) A time-dependent attention convolutional LSTM method for traffic flow prediction. Appl Intell 52(15):17371\u201317386. https:\/\/doi.org\/10.1007\/s10489-022-03324-7","journal-title":"Appl Intell"},{"key":"4980_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.120203","volume":"227","author":"Q Ren","year":"2023","unstructured":"Ren Q, Li Y, Liu Y (2023) Transformer-enhanced periodic temporal convolution network for long short-term traffic flow forecasting. Expert Syst Appl 227:120203. https:\/\/doi.org\/10.1016\/j.eswa.2023.120203","journal-title":"Expert Syst Appl"},{"key":"4980_CR33","doi-asserted-by":"publisher","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed SE, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: IEEE conference on computer vision and pattern recognition pp 1\u20139. https:\/\/doi.org\/10.1109\/CVPR.2015.7298594","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"4980_CR34","doi-asserted-by":"publisher","first-page":"1421","DOI":"10.1007\/s10994-019-05815-0","volume":"108","author":"S-Y Shih","year":"2019","unstructured":"Shih S-Y, Sun F-K, Lee H-y (2019) Temporal pattern attention for multivariate time series forecasting. Mach Learn 108:1421\u20131441","journal-title":"Mach Learn"},{"issue":"14","key":"4980_CR35","doi-asserted-by":"publisher","first-page":"16214","DOI":"10.1007\/s11227-022-04506-3","volume":"78","author":"Q Cheng","year":"2022","unstructured":"Cheng Q, Chen Y, Xiao Y, Yin H, Liu W (2022) A dual-stage attention-based Bi-LSTM network for multivariate time series prediction. J Supercomput 78(14):16214\u201316235. https:\/\/doi.org\/10.1007\/s11227-022-04506-3","journal-title":"J Supercomput"},{"issue":"7","key":"4980_CR36","doi-asserted-by":"publisher","first-page":"5353","DOI":"10.1007\/s10462-020-09822-9","volume":"53","author":"Q Wang","year":"2020","unstructured":"Wang Q, Chen L, Zhao J, Wang W (2020) A deep granular network with adaptive unequal-length granulation strategy for long-term time series forecasting and its industrial applications. Artif Intell Rev 53(7):5353\u20135381. https:\/\/doi.org\/10.1007\/s10462-020-09822-9","journal-title":"Artif Intell Rev"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-04980-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-023-04980-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-04980-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,29]],"date-time":"2023-11-29T14:14:14Z","timestamp":1701267254000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-023-04980-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,2]]},"references-count":36,"journal-issue":{"issue":"23","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["4980"],"URL":"https:\/\/doi.org\/10.1007\/s10489-023-04980-z","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,2]]},"assertion":[{"value":"24 August 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 October 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}