{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T15:03:30Z","timestamp":1781795010920,"version":"3.54.5"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2023,10,25]],"date-time":"2023-10-25T00:00:00Z","timestamp":1698192000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,10,25]],"date-time":"2023-10-25T00:00:00Z","timestamp":1698192000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100012165","name":"Key Technologies Research and Development Program","doi-asserted-by":"publisher","award":["2020YFC1523004"],"award-info":[{"award-number":["2020YFC1523004"]}],"id":[{"id":"10.13039\/501100012165","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012165","name":"Key Technologies Research and Development Program","doi-asserted-by":"publisher","award":["2020YFC1523004"],"award-info":[{"award-number":["2020YFC1523004"]}],"id":[{"id":"10.13039\/501100012165","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SIViP"],"published-print":{"date-parts":[[2024,3]]},"DOI":"10.1007\/s11760-023-02823-5","type":"journal-article","created":{"date-parts":[[2023,10,25]],"date-time":"2023-10-25T17:01:43Z","timestamp":1698253303000},"page":"1015-1025","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Multiscale convolutional neural-based transformer network for time series prediction"],"prefix":"10.1007","volume":"18","author":[{"given":"Zhixing","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yepeng","family":"Guan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,10,25]]},"reference":[{"issue":"1","key":"2823_CR1","doi-asserted-by":"publisher","first-page":"11109","DOI":"10.1007\/s00521-023-08288-4","volume":"35","author":"K Zouaidia","year":"2023","unstructured":"Zouaidia, K., Rais, S., Ghanemi, S.: Weather forecasting based on hybrid decomposition methods and adaptive deep learning strategy. Neural Comput. Appl. 35(1), 11109\u201311124 (2023)","journal-title":"Neural Comput. Appl."},{"issue":"11","key":"2823_CR2","doi-asserted-by":"publisher","first-page":"1536","DOI":"10.3390\/w10111536","volume":"10","author":"A Mosavi","year":"2018","unstructured":"Mosavi, A., Ozturk, P., Chau, K.: Flood prediction using machine learning models: Literature review. Water 10(11), 1536\u20131576 (2018)","journal-title":"Water"},{"issue":"8","key":"2823_CR3","doi-asserted-by":"publisher","first-page":"1585","DOI":"10.3390\/w15081585","volume":"15","author":"R Liu","year":"2023","unstructured":"Liu, R., Zhou, H., Li, D., et al.: Evaluation of artificial precipitation enhancement using UNET-GRU algorithm for rainfall estimation [J]. Water 15(8), 1585\u20131602 (2023)","journal-title":"Water"},{"issue":"2","key":"2823_CR4","doi-asserted-by":"publisher","first-page":"654","DOI":"10.1016\/j.ejor.2017.11.054","volume":"270","author":"T Fischer","year":"2018","unstructured":"Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. Eur. J. Oper. Res. 270(2), 654\u2013669 (2018)","journal-title":"Eur. J. Oper. Res."},{"issue":"1","key":"2823_CR5","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.eswa.2018.03.002","volume":"103","author":"Y Kim","year":"2018","unstructured":"Kim, Y., Won, H.: Forecasting the volatility of stock price index: a hybrid model integrating LSTM with multiple GARCH-type models. Expert Syst. Appl. 103(1), 25\u201337 (2018)","journal-title":"Expert Syst. Appl."},{"issue":"1","key":"2823_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.trc.2017.02.024","volume":"79","author":"G Polson","year":"2017","unstructured":"Polson, G., Sokolov, O.: Deep learning for short-term traffic flow prediction [J]. Transport. Res. Part C: Emerg. Technol. 79(1), 1\u201317 (2017)","journal-title":"Transport. Res. Part C: Emerg. Technol."},{"issue":"1","key":"2823_CR7","doi-asserted-by":"publisher","first-page":"111716","DOI":"10.1016\/j.rse.2020.111716","volume":"241","author":"Q Yuan","year":"2020","unstructured":"Yuan, Q., Shen, H., Li, T., et al.: Deep learning in environmental remote sensing: Achievements and challenges. Remote Sens. Environ. 241(1), 111716\u2013111736 (2020)","journal-title":"Remote Sens. Environ."},{"issue":"5","key":"2823_CR8","doi-asserted-by":"publisher","first-page":"4404","DOI":"10.1109\/TIE.2020.2984443","volume":"68","author":"X Yuan","year":"2020","unstructured":"Yuan, X., Li, L., Yang, C., et al.: Deep learning with spatiotemporal attention-based LSTM for industrial soft sensor model development. IEEE Trans. Industr. Electron. 68(5), 4404\u20134414 (2020)","journal-title":"IEEE Trans. Industr. Electron."},{"issue":"5","key":"2823_CR9","doi-asserted-by":"publisher","first-page":"3168","DOI":"10.1109\/TII.2019.2902129","volume":"16","author":"X Yuan","year":"2019","unstructured":"Yuan, X., Li, L., Yang, C., et al.: Nonlinear dynamic soft sensor modeling with supervised long short-term memory network. IEEE Trans. Industr. Inf. 16(5), 3168\u20133176 (2019)","journal-title":"IEEE Trans. Industr. Inf."},{"issue":"6","key":"2823_CR10","doi-asserted-by":"publisher","first-page":"4788","DOI":"10.1109\/TIE.2018.2864702","volume":"66","author":"L Liu","year":"2018","unstructured":"Liu, L., Hsaio, H., Yao, Tu., et al.: Time series classification with multivariate convolutional neural network. IEEE Trans. Ind. Electr. 66(6), 4788\u20134797 (2018)","journal-title":"IEEE Trans. Ind. Electr."},{"issue":"1","key":"2823_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/srep15508","volume":"5","author":"L Lacasa","year":"2015","unstructured":"Lacasa, L., Nicosia, V., Latora, V.: Network structure of multivariate time series. Sci. Rep. 5(1), 1\u20139 (2015)","journal-title":"Sci. Rep."},{"key":"2823_CR12","doi-asserted-by":"crossref","unstructured":"Zhou, H., Zhang, S., Peng, J., et al.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: AAAI Conference on Artificial Intelligence, pp. 11106\u201311115 (2021)","DOI":"10.1609\/aaai.v35i12.17325"},{"issue":"1","key":"2823_CR13","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1016\/j.patrec.2017.08.009","volume":"105","author":"M Das","year":"2018","unstructured":"Das, M., Ghosh, K.: Data-driven approaches for meteorological time series prediction: a comparative study of the state-of-the-art computational intelligence techniques. Patt. Recogn. Lett. 105(1), 155\u2013164 (2018)","journal-title":"Patt. Recogn. Lett."},{"issue":"1","key":"2823_CR14","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1016\/j.probengmech.2015.12.006","volume":"43","author":"P Johannesson","year":"2016","unstructured":"Johannesson, P., Podg\u00f3rski, K., Rychilk, I., et al.: AR: time series with autoregressive gamma variance for road topography modeling. Probab. Eng. Mech. 43(1), 106\u2013116 (2016)","journal-title":"Probab. Eng. Mech."},{"issue":"1","key":"2823_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12874-021-01235-8","volume":"21","author":"L Schaffer","year":"2021","unstructured":"Schaffer, L., Dobbins, A., Pearon, S.: Interrupted time series analysis using autoregressive integrated moving average (ARIMA) models: a guide for evaluating large-scale health interventions. BMC Med. Res. Methodol. 21(1), 1\u201312 (2021)","journal-title":"BMC Med. Res. Methodol."},{"issue":"1","key":"2823_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/MPE.2005.1","volume":"2016","author":"Q Li","year":"2016","unstructured":"Li, Q., Lin, C.: A new approach for chaotic time series prediction using recurrent neural network. Math. Probl. Eng. 2016(1), 1\u20139 (2016)","journal-title":"Math. Probl. Eng."},{"issue":"1","key":"2823_CR17","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1016\/j.neucom.2017.03.069","volume":"249","author":"X Chen","year":"2017","unstructured":"Chen, X., Chen, X., She, J., et al.: A hybrid time series prediction model based on recurrent neural network and double joint linear\u2013nonlinear extreme learning network for prediction of carbon efficiency in iron ore sintering process. Neurocomputing 249(1), 128\u2013139 (2017)","journal-title":"Neurocomputing"},{"issue":"12","key":"2823_CR18","doi-asserted-by":"publisher","first-page":"13902","DOI":"10.1109\/TCYB.2021.3121312","volume":"52","author":"W Zheng","year":"2021","unstructured":"Zheng, W., Chen, G.: An accurate GRU-based power time-series prediction approach with selective state updating and stochastic optimization. IEEE Trans. Cybernet. 52(12), 13902\u201313914 (2021)","journal-title":"IEEE Trans. Cybernet."},{"issue":"1","key":"2823_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neunet.2019.12.030","volume":"125","author":"Z Karevan","year":"2020","unstructured":"Karevan, Z., Suykens, A.: Transductive LSTM for time-series prediction: an application to weather forecasting. Neural Netw. 125(1), 1\u20139 (2020)","journal-title":"Neural Netw."},{"key":"2823_CR20","doi-asserted-by":"crossref","unstructured":"Dey, R., Salem, M.: Gate-variants of gated recurrent unit (GRU) neural networks. In: International Midwest Symposium on Circuits and Systems, pp. 1597\u20131600 (2017)","DOI":"10.1109\/MWSCAS.2017.8053243"},{"issue":"7","key":"2823_CR21","doi-asserted-by":"publisher","first-page":"1235","DOI":"10.1162\/neco_a_01199","volume":"31","author":"Y Yu","year":"2019","unstructured":"Yu, Y., Si, X., Hu, C., et al.: A review of recurrent neural networks: LSTM cells and network architectures. Neural Comput. 31(7), 1235\u20131270 (2019)","journal-title":"Neural Comput."},{"issue":"1","key":"2823_CR22","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.procs.2019.08.007","volume":"155","author":"S Hwang","year":"2019","unstructured":"Hwang, S., Jeon, G., Jeong, J., et al.: A novel time series based Seq2Seq model for temperature prediction in firing furnace process. Procedia Comput. Sci. 155(1), 19\u201326 (2019)","journal-title":"Procedia Comput. Sci."},{"issue":"4","key":"2823_CR23","doi-asserted-by":"publisher","first-page":"3443","DOI":"10.1007\/s11063-022-10774-0","volume":"54","author":"X Wang","year":"2022","unstructured":"Wang, X., Cai, Z., Wen, Z., et al.: Long time series deep forecasting with multiscale feature extraction and Seq2seq attention mechanism. Neural Process. Lett. 54(4), 3443\u20133466 (2022)","journal-title":"Neural Process. Lett."},{"issue":"4","key":"2823_CR24","doi-asserted-by":"publisher","first-page":"4285","DOI":"10.1109\/TIE.2021.3073301","volume":"69","author":"H Chen","year":"2021","unstructured":"Chen, H., Zhang, X.: Path planning for intelligent vehicle collision avoidance of dynamic pedestrian using Att-LSTM, MSFM, and MPC at unsignalized crosswalk. IEEE Trans. Industr. Electron. 69(4), 4285\u20134295 (2021)","journal-title":"IEEE Trans. Industr. Electron."},{"key":"2823_CR25","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 1\u201311 (2017)"},{"key":"2823_CR26","doi-asserted-by":"crossref","unstructured":"Zerveas, G., Jayaraman, S., Patel, D., et al.: A transformer-based framework for multivariate time series representation learning. In: Knowledge Discovery & Data Mining, pp. 2114\u20132124 (2021)","DOI":"10.1145\/3447548.3467401"},{"key":"2823_CR27","unstructured":"Li, S., Jin, X., Yao, X., et al.: Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting. In: Advances in Neural Information Processing Systems, pp. 1\u201314 (2019)"},{"key":"2823_CR28","unstructured":"Li, Z., Cai, J., He, S., et al.: Seq2seq dependency parsing. In: Computational Linguistics, pp. 3203\u20133214 (2018)"},{"key":"2823_CR29","doi-asserted-by":"crossref","unstructured":"Sun, F., Liu, J., Wu, J., et al.: BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformer. In: Information and Knowledge Management, pp. 1441\u20131450 (2019)","DOI":"10.1145\/3357384.3357895"},{"issue":"3","key":"2823_CR30","doi-asserted-by":"publisher","first-page":"1521","DOI":"10.1109\/TII.2021.3086798","volume":"18","author":"Z Geng","year":"2021","unstructured":"Geng, Z., Chen, Z., Meng, Q., et al.: Novel transformer based on gated convolutional neural network for dynamic soft sensor modeling of industrial processes. IEEE Trans. Industr. Inf. 18(3), 1521\u20131529 (2021)","journal-title":"IEEE Trans. Industr. Inf."},{"key":"2823_CR31","doi-asserted-by":"crossref","unstructured":"Lai, G., Chang, C., Yang, Y., et al.: Modeling long-and short-term temporal patterns with deep neural networks. In: Research & Development in Information Retrieval, pp. 95\u2013104 (2018)","DOI":"10.1145\/3209978.3210006"},{"issue":"2","key":"2823_CR32","doi-asserted-by":"publisher","first-page":"750","DOI":"10.1016\/j.snb.2007.09.060","volume":"129","author":"S Vito","year":"2008","unstructured":"Vito, S., Massera, E., Piga, M., et al.: On field calibration of an electronic nose for benzene estimation in an urban pollution monitoring scenario. Sens. Actuators, B Chem. 129(2), 750\u2013757 (2008)","journal-title":"Sens. Actuators, B Chem."},{"issue":"1","key":"2823_CR33","first-page":"775","volume":"237","author":"T Afrin","year":"2022","unstructured":"Afrin, T., Nita, Y.: A long short-term memory-based correlated traffic data prediction framework. Knowl.-Based Syst. 237(1), 775\u2013786 (2022)","journal-title":"Knowl.-Based Syst."},{"issue":"1","key":"2823_CR34","doi-asserted-by":"publisher","first-page":"106348","DOI":"10.1016\/j.knosys.2020.106348","volume":"206","author":"J Zhang","year":"2020","unstructured":"Zhang, J., Hao, K., Tang, X., et al.: A multi-feature fusion model for Chinese relation extraction with entity sense. Knowl.-Based Syst. 206(1), 106348\u2013106358 (2020)","journal-title":"Knowl.-Based Syst."},{"key":"2823_CR35","unstructured":"Tang, B., Matteson, S.: Probabilistic transformer for time series analysis. In: Advances in Neural Information Processing Systems, pp. 23592\u201323608 (2021)"}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-023-02823-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-023-02823-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-023-02823-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,20]],"date-time":"2024-02-20T07:03:13Z","timestamp":1708412593000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-023-02823-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,25]]},"references-count":35,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,3]]}},"alternative-id":["2823"],"URL":"https:\/\/doi.org\/10.1007\/s11760-023-02823-5","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,25]]},"assertion":[{"value":"13 August 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 September 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 October 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 October 2023","order":4,"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 conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This paper does not contain any studies involving humans or animals. This declaration is not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}