{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T10:45:10Z","timestamp":1772793910158,"version":"3.50.1"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2022,7,30]],"date-time":"2022-07-30T00:00:00Z","timestamp":1659139200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,7,30]],"date-time":"2022-07-30T00:00:00Z","timestamp":1659139200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"The Key Research and Development Program of the Sichuan Province","award":["2022YFQ0047"],"award-info":[{"award-number":["2022YFQ0047"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2023,4]]},"DOI":"10.1007\/s10489-022-03859-9","type":"journal-article","created":{"date-parts":[[2022,7,30]],"date-time":"2022-07-30T02:02:32Z","timestamp":1659146552000},"page":"8160-8179","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Fractional-order multiscale attention feature pyramid network for time series classification"],"prefix":"10.1007","volume":"53","author":[{"given":"Wen","family":"Pan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2680-6814","authenticated-orcid":false,"given":"Weihua","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yifei","family":"Pu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,7,30]]},"reference":[{"key":"3859_CR1","first-page":"1","volume":"71","author":"Z Hegui","year":"2022","unstructured":"Hegui Z, Ruoyang X, Jiapeng Z, Jinhai L, Chengqing L, Lianping Y (2022) A driving behavior risk classification framework via the unbalanced time series samples. IEEE Trans Instrum Meas 71:1\u201312","journal-title":"IEEE Trans Instrum Meas"},{"issue":"6","key":"3859_CR2","doi-asserted-by":"publisher","first-page":"4788","DOI":"10.1109\/TIE.2018.2864702","volume":"66","author":"L Chien-Liang","year":"2019","unstructured":"Chien-Liang L, Wen-Hoar H, Yao-Chung T (2019) Time series classification with multivariate convolutional neural network. IEEE Trans Ind Electron 66(6):4788\u20134797","journal-title":"IEEE Trans Ind Electron"},{"issue":"1","key":"3859_CR3","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1145\/1882471.1882478","volume":"12","author":"X Z","year":"2010","unstructured":"Z X, J P, E K (2010) A brief survey on sequence classification. Acm Sigkdd Explor Newsl 12(1):40\u201348","journal-title":"Acm Sigkdd Explor Newsl"},{"key":"3859_CR4","first-page":"10","volume":"47","author":"A Sharabiani","year":"2017","unstructured":"Sharabiani A, Darabi H, Rezaei A, Harford S, Johnson H, Karim F (2017) Efficient classification of long time series by 3-d dynamic time warping. IEEE Trans Syst 47:10","journal-title":"IEEE Trans Syst"},{"key":"3859_CR5","doi-asserted-by":"publisher","first-page":"17829","DOI":"10.1109\/ACCESS.2021.3053703","volume":"9","author":"C Zhi","year":"2021","unstructured":"Zhi C, Yongguo L, Jiajing Z, Yun Z, Qiaoqin L, Rongjiang J, Xia H (2021) Deep multiple metric learning for time series classification. IEEE Access 9:17829\u201317842","journal-title":"IEEE Access"},{"issue":"6","key":"3859_CR6","doi-asserted-by":"publisher","first-page":"1505","DOI":"10.1007\/s10618-014-0377-7","volume":"29","author":"P Sch\u00e4fer","year":"2015","unstructured":"Sch\u00e4fer P (2015) The boss is concerned with time series classification in the presence of noise. Data Mining Knowl Discov 29(6):1505\u20131530","journal-title":"Data Mining Knowl Discov"},{"issue":"27","key":"3859_CR7","doi-asserted-by":"publisher","first-page":"2522","DOI":"10.1109\/TKDE.2015.2416723","volume":"9","author":"BAnthony Bagnall","year":"2015","unstructured":"Bagnall BAnthony, Lines J, Hills J, Bostrom A (2015) Time-series classification with cote: the collective of transformation-based ensembles. IEEE Trans Knowl Data Eng 9(27):2522\u20132535","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"3859_CR8","doi-asserted-by":"crossref","unstructured":"Lines, J, Taylor, S, Bagnall, A (2016) Hive-cote: the hierarchical vote collective of transformation-based ensembles for time series classification, 1548\u20131549","DOI":"10.1109\/ICDM.2016.0133"},{"key":"3859_CR9","doi-asserted-by":"crossref","unstructured":"Wang Z, Yan W, Oates T (2017) Time series classification from scratch with deep neural networks: a strong baseline in neural networks (ijcnn). In: 2017 International joint conference on, pp 1578\u20131585","DOI":"10.1109\/IJCNN.2017.7966039"},{"key":"3859_CR10","doi-asserted-by":"publisher","first-page":"1660","DOI":"10.1109\/ACCESS.2017.2779939","volume":"06","author":"F Karim","year":"2018","unstructured":"Karim F, Majumdar S, Darabi H, Chen S (2018) Lstm fully convolutional networks for time series classification. IEEE Access 06:1660\u20131669","journal-title":"IEEE Access"},{"key":"3859_CR11","first-page":"2285","volume":"30","author":"C Zipeng","year":"2021","unstructured":"Zipeng C, Qianli M, Zhenxi L (2021) Time-aware multi-scale rnns for time series modeling. Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence 30:2285\u20132291","journal-title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence"},{"key":"3859_CR12","doi-asserted-by":"crossref","unstructured":"Cui Z, Chen W, Chen Y (2016) Multi-scale convolutional neural networks for time series classification","DOI":"10.14257\/ijgdc.2016.9.11.06"},{"key":"3859_CR13","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1016\/j.neunet.2021.01.001","volume":"136","author":"W Chen","year":"2020","unstructured":"Chen W, Shi K (2020) Mulit-scale attention convlutional neural network for time series. Neural Netw 136:126\u2013140","journal-title":"Neural Netw"},{"key":"3859_CR14","doi-asserted-by":"publisher","first-page":"877","DOI":"10.1002\/asmb.2536","volume":"36","author":"PA Parker","year":"2020","unstructured":"Parker P A, Holan S H, Ravishanker N (2020) Nonlinear time series classification using bispectrum-baseddeep convolutional neural networks. Appl Stoch Model Bus Ind 36:877\u2013890","journal-title":"Appl Stoch Model Bus Ind"},{"key":"3859_CR15","doi-asserted-by":"crossref","unstructured":"Windsor E, Cao W (2022) Improving exchange rate forecasting via a new deep multimodal fusion model. Appl Intell","DOI":"10.1007\/s10489-022-03342-5"},{"key":"3859_CR16","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1109\/TCYB.2018.2890397","volume":"49","author":"H Chen","year":"2019","unstructured":"zhichen Gong, Chen H, Yuan B, Yao X (2019) Multiobjective learning in the model space for time series classification. IEEE Trans Cybern 49:3","journal-title":"IEEE Trans Cybern"},{"key":"3859_CR17","first-page":"936","volume":"06","author":"T-Y Lin","year":"2017","unstructured":"Lin T-Y, Dollar P, Cirshick R, He K, Hariharan B, Belongie S (2017) Feature pyramid networks for object detection. IEEE Conf Comput Vis Pattern Recogn 06:936\u2013944","journal-title":"IEEE Conf Comput Vis Pattern Recogn"},{"key":"3859_CR18","volume-title":"The fractional calculus: integrations and differentiations of arbitrary order","author":"KB Oldham","year":"1974","unstructured":"Oldham K B, Spanier (1974) The fractional calculus: integrations and differentiations of arbitrary order. Academic, New York"},{"key":"3859_CR19","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1109\/TIP.2009.2035980","volume":"02","author":"Y-F Pu","year":"2010","unstructured":"Pu Y-F, Zhou J-L, Yuan X (2010) Fractional differential mask:a fractional differential-based approach for multiscale texture enhancement. IEEE Trans Image Process 02:491\u2013511","journal-title":"IEEE Trans Image Process"},{"key":"3859_CR20","doi-asserted-by":"crossref","unstructured":"Yang S, Gao T, Wang J, Deng B, Lansdell B, Linares-Barrance B (2021) Efficient spike-driven learning with dendritic event-based processing. Front Neurosci","DOI":"10.3389\/fnins.2021.601109"},{"key":"3859_CR21","doi-asserted-by":"crossref","unstructured":"Yang S, wang J, Deng B, Azghadi M R, Linares-Barranco B (2021) Neuromorphic context-dependent learning framework with fault-tolerant spike routing. IEEE Transactions on Neural Networks and Learning Systems","DOI":"10.1109\/TNNLS.2021.3084250"},{"key":"3859_CR22","doi-asserted-by":"publisher","first-page":"917","DOI":"10.1007\/s10618-019-00619-1","volume":"33","author":"HI Fawaz","year":"2019","unstructured":"Fawaz H I, Forestier G, Weber J, Idoumghar L, Muller P-A (2019) Deep learning for time series classification: a review. Data Min Knowl Disc 33:917\u2013963","journal-title":"Data Min Knowl Disc"},{"key":"3859_CR23","volume-title":"Fractional calculus","author":"AC McBride","year":"1986","unstructured":"McBride A C (1986) Fractional calculus. Halsted Press, New York"},{"key":"3859_CR24","volume-title":"Fractional calculus","author":"K Nishimoto","year":"1989","unstructured":"Nishimoto K (1989) Fractional calculus. Halsted Press, New York"},{"key":"3859_CR25","first-page":"238","volume-title":"A fractional-order variational residual cnn for low dose ct image denoising","author":"M Chen","year":"2019","unstructured":"Chen M, Pu Y-F, Bai Y-C (2019) A fractional-order variational residual cnn for low dose ct image denoising. Spring, Switzerland, pp 238\u2013249"},{"key":"3859_CR26","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez NA, Kaiser L (2017) Attention is all you need"},{"key":"3859_CR27","doi-asserted-by":"publisher","first-page":"2011","DOI":"10.1109\/TPAMI.2019.2913372","volume":"42","author":"J Hu","year":"2020","unstructured":"Hu J, Shen L, Albanie S, Sun G, Wu E (2020) Squeeze-and-excitation networks. IEEE Trans Pattern Anal Mach Intell 42:2011\u20132023","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3859_CR28","unstructured":"Park J, Woo S, Lee J-Y, Kweon I S (2017) Bam: bottleneck attention moudule"},{"key":"3859_CR29","doi-asserted-by":"crossref","unstructured":"Woo S, Park J, Lee J-Y, Kweon I S (2018) Cbam: convoltional block attention module, 3\u201319","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"3859_CR30","unstructured":"Ioffe S, Szegedy C (2015) Batch normalization:accelerating deep network training by reducing internal covariate shift, 448\u2013456"},{"key":"3859_CR31","unstructured":"Kingma D, Ba J (2014) Adam: a method for stochastic optimization"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-03859-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-022-03859-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-03859-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,25]],"date-time":"2023-11-25T04:13:11Z","timestamp":1700885591000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-022-03859-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,30]]},"references-count":31,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2023,4]]}},"alternative-id":["3859"],"URL":"https:\/\/doi.org\/10.1007\/s10489-022-03859-9","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,30]]},"assertion":[{"value":"7 June 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 July 2022","order":2,"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 known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Competing interests"}}]}}