{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,20]],"date-time":"2026-06-20T04:08:02Z","timestamp":1781928482632,"version":"3.54.5"},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2020,2,22]],"date-time":"2020-02-22T00:00:00Z","timestamp":1582329600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,2,22]],"date-time":"2020-02-22T00:00:00Z","timestamp":1582329600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"name":"National Key R&D Program","award":["2017YFA0700300"],"award-info":[{"award-number":["2017YFA0700300"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Sciences Foundation of China","doi-asserted-by":"crossref","award":["61533005"],"award-info":[{"award-number":["61533005"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Artif Intell Rev"],"published-print":{"date-parts":[[2020,10]]},"DOI":"10.1007\/s10462-020-09822-9","type":"journal-article","created":{"date-parts":[[2020,2,22]],"date-time":"2020-02-22T15:02:45Z","timestamp":1582383765000},"page":"5353-5381","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["A deep granular network with adaptive unequal-length granulation strategy for long-term time series forecasting and its industrial applications"],"prefix":"10.1007","volume":"53","author":[{"given":"Qiang","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Long","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3573-152X","authenticated-orcid":false,"given":"Jun","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,2,22]]},"reference":[{"issue":"1","key":"9822_CR1","first-page":"222","volume":"1648","author":"A Alexandridis","year":"2015","unstructured":"Alexandridis A, Famelis IT, Tsitouras C (2015) Long-term time-series prediction using radial basis function neural networks. AIP Conf 1648(1):222\u2013228","journal-title":"AIP Conf"},{"issue":"2","key":"9822_CR2","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.jfds.2018.02.002","volume":"4","author":"A Atkins","year":"2018","unstructured":"Atkins A, Niranjan M, Gerding E (2018) Financial news predicts stock market volatility better than close price. J Finance Data Sci 4(2):120\u2013137","journal-title":"J Finance Data Sci"},{"issue":"10","key":"9822_CR3","doi-asserted-by":"crossref","first-page":"2572","DOI":"10.1109\/TSP.2018.2816587","volume":"66","author":"A Bonvard","year":"2018","unstructured":"Bonvard A, Houcke S, Gautier R, Marazin M (2018) Classification based on Euclidean distance distribution for blind identification of error correcting codes in noncooperative contexts. IEEE Trans Signal Process 66(10):2572\u20132583","journal-title":"IEEE Trans Signal Process"},{"issue":"5","key":"9822_CR4","doi-asserted-by":"crossref","first-page":"2195","DOI":"10.1002\/mrm.25805","volume":"75","author":"G Carluccio","year":"2016","unstructured":"Carluccio G, Bruno M, Collins CM (2016) Predicting long-term temperature increase for time-dependent SAR levels with a single short-term temperature response. Magn Reson Med 75(5):2195\u20132203","journal-title":"Magn Reson Med"},{"issue":"12","key":"9822_CR5","doi-asserted-by":"crossref","first-page":"3123","DOI":"10.1109\/TNNLS.2015.2404823","volume":"26","author":"R Chandra","year":"2015","unstructured":"Chandra R (2015) Competition and collaboration in cooperative coevolution of Elman recurrent neural networks for time-series prediction. IEEE Trans Neural Netw Learn Syst 26(12):3123\u20133136","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"2","key":"9822_CR6","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.ins.2014.09.038","volume":"294","author":"MY Chen","year":"2015","unstructured":"Chen MY, Chen BT (2015) A hybrid fuzzy time series model based on granular computing for stock price forecasting. Inf Sci 294(2):227\u2013241","journal-title":"Inf Sci"},{"key":"9822_CR7","doi-asserted-by":"crossref","first-page":"681","DOI":"10.1016\/j.enconman.2018.03.098","volume":"165","author":"J Chen","year":"2018","unstructured":"Chen J, Zeng GQ, Zhou WN, Du W, Lu KD (2018) Wind speed forecasting using nonlinear-learning ensemble of deep learning time series prediction and extremal optimization. Energy Convers Manag 165:681\u2013695","journal-title":"Energy Convers Manag"},{"key":"9822_CR8","doi-asserted-by":"crossref","unstructured":"Chiong R, Fan ZW, Hu ZY, Adam MTP, Lutz B, Neumann D (2018) A sentiment analysis-based machine learning approach for financial market prediction via news disclosures. In: Proceedings of the genetic and evolutionary computation conference companion, Kyoto, pp 278\u2013279","DOI":"10.1145\/3205651.3205682"},{"key":"9822_CR9","unstructured":"Artificial neural network and computational intelligence forecasting competition. http:\/\/www.neural-forecasting-competition.com\/. Accessed 02 May 2019"},{"key":"9822_CR10","doi-asserted-by":"crossref","first-page":"883","DOI":"10.1016\/j.fuel.2017.09.104","volume":"211","author":"AM Cormos","year":"2018","unstructured":"Cormos AM, Dinca C, Petrescu L, Chisalita DA, Szima S, Cormos CC (2018) Carbon capture and utilisation technologies applied to energy conversion systems and other energy-intensive industrial applications. Fuel 211:883\u2013890","journal-title":"Fuel"},{"key":"9822_CR11","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1016\/j.renene.2016.02.003","volume":"92","author":"B Doucoure","year":"2016","unstructured":"Doucoure B, Agbossou K, Cardenas A (2016) Time series prediction using artificial wavelet neural network and multi-resolution analysis: application to wind speed data. Renew Energy 92:202\u2013211","journal-title":"Renew Energy"},{"issue":"2","key":"9822_CR12","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1007\/s10462-011-9308-9","volume":"41","author":"CP Faustino","year":"2014","unstructured":"Faustino CP, Novaes CP, Pinheiro CA, Carpinteiro O (2014) Improving the performance of fuzzy rules-based forecasters through application of FCM algorithm. Artif Intell Rev 41(2):287\u2013300","journal-title":"Artif Intell Rev"},{"key":"9822_CR13","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.knosys.2016.10.017","volume":"115","author":"W Froelich","year":"2017","unstructured":"Froelich W, Pedrycz W (2017) Fuzzy cognitive maps in the modeling of granular time series. Knowl-Based Syst 115:110\u2013122","journal-title":"Knowl-Based Syst"},{"key":"9822_CR14","doi-asserted-by":"crossref","first-page":"81934","DOI":"10.1109\/ACCESS.2019.2923637","volume":"7","author":"XL Fu","year":"2019","unstructured":"Fu XL, Zhang S, Chen JP, Ouyang TX, Wu J (2019) A sentiment-aware trading volume prediction model for P2P market using LSTM. IEEE Access 7:81934\u201381944","journal-title":"IEEE Access"},{"key":"9822_CR15","doi-asserted-by":"crossref","first-page":"590","DOI":"10.1016\/j.asoc.2014.06.030","volume":"27","author":"A Gacek","year":"2015","unstructured":"Gacek A (2015) Signal processing and time series description: a perspective of computational intelligence and granular computing. Appl Soft Comput 27:590\u2013601","journal-title":"Appl Soft Comput"},{"key":"9822_CR16","doi-asserted-by":"crossref","first-page":"1429","DOI":"10.1007\/s00208-016-1416-z","volume":"367","author":"M Garcia-Fernandez","year":"2017","unstructured":"Garcia-Fernandez M, Ross J (2017) Balanced metrics on twisted Higgs bundles. Math Ann 367:1429\u20131471","journal-title":"Math Ann"},{"key":"9822_CR17","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.neunet.2013.12.002","volume":"51","author":"A Grigorievskiy","year":"2014","unstructured":"Grigorievskiy A, Michea Y, Ventel\u00e4 AM, S\u00e9verin E, Lendasse A (2014) Long-term time series prediction using OP-ELM. Neural Netw 51:50\u201356","journal-title":"Neural Netw"},{"issue":"8","key":"9822_CR18","doi-asserted-by":"crossref","first-page":"2402","DOI":"10.1109\/TCYB.2017.2739338","volume":"48","author":"Y Guo","year":"2018","unstructured":"Guo Y, Jiao L, Wang S, Wang S, Liu F (2018) Fuzzy sparse autoencoder framework for single image per person face recognition. IEEE Trans Cybern 48(8):2402\u20132415","journal-title":"IEEE Trans Cybern"},{"issue":"10","key":"9822_CR19","doi-asserted-by":"crossref","first-page":"4830","DOI":"10.1016\/j.eswa.2015.01.060","volume":"42","author":"RA Hmouz","year":"2015","unstructured":"Hmouz RA, Pedrycz W, Balamash A (2015) Description and prediction of time series: a general framework of Granular Computing. Expert Syst Appl 42(10):4830\u20134839","journal-title":"Expert Syst Appl"},{"issue":"6","key":"9822_CR20","doi-asserted-by":"crossref","first-page":"1786","DOI":"10.1109\/TITS.2015.2511156","volume":"17","author":"Z Hou","year":"2016","unstructured":"Hou Z, Li X (2016) Repeatability and similarity of freeway traffic flow and long-term prediction under big data. IEEE Trans Intell Transp Syst 17(6):1786\u20131796","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"9822_CR21","doi-asserted-by":"crossref","first-page":"644","DOI":"10.1016\/j.asoc.2014.11.024","volume":"47","author":"O Hryniewicz","year":"2016","unstructured":"Hryniewicz O, Kaczmarek K (2016) Bayesian analysis of time series using granular computing approach. Appl Soft Comput 47:644\u2013652","journal-title":"Appl Soft Comput"},{"key":"9822_CR22","doi-asserted-by":"crossref","unstructured":"Jiang XJ, Zhang YH, Zhang WS, Xiao X (2013) A novel sparse auto-encoder for deep unsupervised learning. In: International conference on advanced computational intelligence, Hangzhou: IEEE Computer Society, 2013, pp 256\u2013261","DOI":"10.1109\/ICACI.2013.6748512"},{"key":"9822_CR23","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1016\/j.chaos.2019.04.008","volume":"123","author":"M Jokar","year":"2019","unstructured":"Jokar M, Salarieh H, Alasty A (2019) On the existence of proper stochastic Markov models for statistical reconstruction and prediction of chaotic time series. Chaos Solitons Fractals 123:373\u2013382","journal-title":"Chaos Solitons Fractals"},{"key":"9822_CR24","doi-asserted-by":"crossref","first-page":"1928","DOI":"10.1016\/j.energy.2017.10.142","volume":"141","author":"H Jouhara","year":"2017","unstructured":"Jouhara H, Almahmoud S, Chauhan A, Delpech B, Bianchi G, Tassou SA, Llera R, Lago F, Arribas JJ (2017) Experimental and theoretical investigation of a flat heat pipe heat exchanger for waste heat recovery in the steel industry. Energy 141:1928\u20131939","journal-title":"Energy"},{"key":"9822_CR25","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.energy.2017.03.009","volume":"126","author":"SHA Kaboli","year":"2017","unstructured":"Kaboli SHA, Fallahpour A, Selvaraj J, Rahim NA (2017) Long-term electrical energy consumption formulating and forecasting via optimized gene expression programming. Energy 126:144\u2013164","journal-title":"Energy"},{"key":"9822_CR26","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.jhydrol.2015.12.014","volume":"534","author":"O Kisi","year":"2016","unstructured":"Kisi O, Parmar K (2016) Application of least square support vector machine and multivariate adaptive regression spline models in long term prediction of river water pollution. J Hydrol 534:104\u2013112","journal-title":"J Hydrol"},{"key":"9822_CR27","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1016\/j.energy.2019.02.020","volume":"173","author":"H Lee","year":"2019","unstructured":"Lee H, Eom J, Cho C, Koo Y (2019) A bottom-up model of industrial energy system with positive mathematical programming. Energy 173:679\u2013690","journal-title":"Energy"},{"key":"9822_CR28","unstructured":"Li JH, Bu H, Wu JJ (2017) Sentiment-aware stock market prediction: a deep learning method. In: 2017 international conference on service systems and service management, Dalian"},{"key":"9822_CR29","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.engappai.2018.02.012","volume":"71","author":"S Liu","year":"2018","unstructured":"Liu S, Pedrycz W, Gacek A, Dai Y (2018) Development of information granules of higher type and their applications to granular models of time series. Eng Appl Artif Intell 71:60\u201372","journal-title":"Eng Appl Artif Intell"},{"issue":"2","key":"9822_CR30","first-page":"865","volume":"16","author":"YS Lv","year":"2015","unstructured":"Lv YS, Duan YJ, Kang WW, Li ZX, Wang FY (2015) Traffic flow prediction with big data: a deep learning approach. IEEE Trans Intell Transp Syst 16(2):865\u2013873","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"4300","key":"9822_CR31","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1126\/science.267326","volume":"197","author":"MC Mackey","year":"1977","unstructured":"Mackey MC, Glass L (1977) Oscillation and chaos in physiological control systems. Science 197(4300):287\u2013289","journal-title":"Science"},{"key":"9822_CR32","doi-asserted-by":"crossref","unstructured":"Masci J, Meier U, Cire\u015fan D, Schmidhuber J (2011) Stacked convolutional auto-encoders for hierarchical feature extraction. In: International conference on artificial neural networks and machine learning, Springer Berlin Heidelberg, pp 52\u201359","DOI":"10.1007\/978-3-642-21735-7_7"},{"issue":"1","key":"9822_CR33","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1007\/s10462-018-09679-z","volume":"52","author":"G Nguyen","year":"2019","unstructured":"Nguyen G, Dlugolinsky S, Bob\u00e1k M, Tran V, Garc\u00eda \u00c1L, Heredia I, Mal\u00edk P, Hluch\u00fd L (2019) Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey. Artif Intell Rev 52(1):77\u2013124","journal-title":"Artif Intell Rev"},{"key":"9822_CR34","first-page":"157","volume":"254","author":"MZ Petrovic","year":"2015","unstructured":"Petrovic MZ, Stanimirovic PS (2015) Representations and computations of {2, 3\u2009~} and {2, 4\u2009~}-inverses in indefinite inner product spaces. Appl Math Comput 254:157\u2013171","journal-title":"Appl Math Comput"},{"issue":"1","key":"9822_CR35","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1007\/s10462-018-9630-6","volume":"52","author":"Z Pezeshki","year":"2019","unstructured":"Pezeshki Z, Mazinani SM (2019) Comparison of artificial neural networks, fuzzy logic and neuro fuzzy for predicting optimization of building thermal consumption: a survey. Artif Intell Rev 52(1):495\u2013525","journal-title":"Artif Intell Rev"},{"issue":"5","key":"9822_CR36","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1016\/0375-9601(76)90101-8","volume":"57","author":"OE Rossler","year":"1976","unstructured":"Rossler OE (1976) An equation for continuous chaos. Phys Lett A 57(5):397\u2013398","journal-title":"Phys Lett A"},{"key":"9822_CR37","doi-asserted-by":"crossref","unstructured":"Shen H, Liang X (2016) A time series forecasting model based on deep learning integrated algorithm with stacked autoencoders and SVR for FX Prediction. In: International conference on artificial neural networks and machine learning, Springer Cham, pp 326\u2013335","DOI":"10.1007\/978-3-319-44781-0_39"},{"key":"9822_CR38","doi-asserted-by":"crossref","first-page":"524","DOI":"10.1016\/j.asoc.2017.10.038","volume":"62","author":"LH Shen","year":"2018","unstructured":"Shen LH, Chen JH, Zeng ZG, Yang JZ, Jin J (2018) A novel echo state network for multivariate and nonlinear time series prediction. Appl Soft Comput 62:524\u2013535","journal-title":"Appl Soft Comput"},{"key":"9822_CR39","doi-asserted-by":"crossref","first-page":"784","DOI":"10.1038\/nnano.2017.83","volume":"12","author":"PM Sheridan","year":"2017","unstructured":"Sheridan PM, Cai F, Du C, Ma W, Zhang Z, Lu W (2017) Sparse coding with memristor networks. Nat Nanotechnol 12:784\u2013789","journal-title":"Nat Nanotechnol"},{"key":"9822_CR40","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/j.jocs.2018.05.008","volume":"27","author":"P Singh","year":"2018","unstructured":"Singh P, Dhiman G (2018) A hybrid fuzzy time series forecasting model based on granular computing and bio-inspired optimization approaches. J Comput Sci 27:370\u2013385","journal-title":"J Comput Sci"},{"issue":"6","key":"9822_CR41","doi-asserted-by":"crossref","first-page":"1713","DOI":"10.1109\/TMM.2014.2329648","volume":"16","author":"E Spyromitros","year":"2014","unstructured":"Spyromitros E, Papadopoulos S, Kompatsiaris I, Tsoumakas G, Vlahavas I (2014) A comprehensive study over VLAD and product quantization in large-scale image retrieval. IEEE Trans Multimedia 16(6):1713\u20131728","journal-title":"IEEE Trans Multimedia"},{"issue":"12","key":"9822_CR42","first-page":"3371","volume":"11","author":"P Vincent","year":"2010","unstructured":"Vincent P, Larochelle H, Lajoie I, Bengio Y, Antoine MP (2010) Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J Mach Learn Res 11(12):3371\u20133408","journal-title":"J Mach Learn Res"},{"issue":"2","key":"9822_CR43","first-page":"173","volume":"22","author":"YL Wang","year":"2017","unstructured":"Wang YL (2017) Stock market forecasting with financial micro-blog based on sentiment and time series analysis. Science 22(2):173\u2013179","journal-title":"Science"},{"issue":"7","key":"9822_CR44","doi-asserted-by":"crossref","first-page":"6258","DOI":"10.1109\/TVT.2016.2635161","volume":"66","author":"J Wang","year":"2017","unstructured":"Wang J, Zhang X, Gao Q, Yue H, Wang H (2017) Device-free wireless localization and activity recognition: a deep learning approach. IEEE Trans Veh Technol 66(7):6258\u20136267","journal-title":"IEEE Trans Veh Technol"},{"key":"9822_CR45","doi-asserted-by":"crossref","unstructured":"Xing FZ, Cambria E, Zou XM (2017) Predicting evolving chaotic time series with fuzzy neural networks. In: 2017 international joint conference on neural networks, Anchorage, 2017, pp 3176\u20133183","DOI":"10.1109\/IJCNN.2017.7966252"},{"issue":"4","key":"9822_CR46","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1109\/MCI.2018.2866727","volume":"13","author":"FZ Xing","year":"2018","unstructured":"Xing FZ, Cambria E, Welsch RE (2018a) Intelligent asset allocation via market sentiment views. IEEE Comput Intell Mag 13(4):25\u201334","journal-title":"IEEE Comput Intell Mag"},{"issue":"1","key":"9822_CR47","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1007\/s10462-017-9588-9","volume":"50","author":"FZ Xing","year":"2018","unstructured":"Xing FZ, Cambria E, Welsch RE (2018b) Natural language based financial forecasting: a survey. Artif Intell Rev 50(1):49\u201373","journal-title":"Artif Intell Rev"},{"key":"9822_CR48","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.knosys.2019.03.029","volume":"176","author":"FZ Xing","year":"2019","unstructured":"Xing FZ, Cambria E, Zhang Y (2019) Sentiment-aware volatility forecasting. Knowl-Based Syst 176:68\u201376","journal-title":"Knowl-Based Syst"},{"issue":"6","key":"9822_CR49","doi-asserted-by":"crossref","first-page":"1621","DOI":"10.1109\/TNNLS.2018.2869131","volume":"30","author":"ML Xu","year":"2019","unstructured":"Xu ML, Yang YZ, Han M, Qiu T, Lin HF (2019) Spatio-temporal interpolated echo state network for meteorological series prediction. IEEE Trans Neural Netw Learn Syst 30(6):1621\u20131634","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"3","key":"9822_CR50","doi-asserted-by":"crossref","first-page":"2054","DOI":"10.1109\/TWC.2015.2497692","volume":"15","author":"P Yang","year":"2016","unstructured":"Yang P, Guan YL, Xiao Y, Renzo MD, Li SQ, Hanzo L (2016) Transmit pre-coded spatial modulation: maximizing the minimum Euclidean distance versus minimizing the bit error ratio. IEEE Trans Wireless Commun 15(3):2054\u20132068","journal-title":"IEEE Trans Wireless Commun"},{"issue":"9","key":"9822_CR51","doi-asserted-by":"crossref","first-page":"4888","DOI":"10.1016\/j.jfranklin.2019.01.027","volume":"356","author":"XS Yao","year":"2019","unstructured":"Yao XS, Wang ZS (2019) Broad echo state network for multivariate time series prediction. J Franklin Inst 356(9):4888\u20134906","journal-title":"J Franklin Inst"},{"issue":"6","key":"9822_CR52","doi-asserted-by":"crossref","first-page":"1977","DOI":"10.1109\/TSMCC.2012.2236648","volume":"43","author":"JT Yao","year":"2013","unstructured":"Yao JT, Vasilakos AV, Pedrycz W (2013) Granular computing: perspectives and challenges. IEEE Trans Cybern 43(6):1977\u20131989","journal-title":"IEEE Trans Cybern"},{"key":"9822_CR53","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.cageo.2016.03.002","volume":"9","author":"H Yoon","year":"2016","unstructured":"Yoon H, Hyun YJ, Ha K, Lee KK, Kim GB (2016) A method to improve the stability and accuracy of ANN- and SVM-based time series models for long-term groundwater level predictions. Comput Geosci 9:144\u2013155","journal-title":"Comput Geosci"},{"key":"9822_CR54","doi-asserted-by":"crossref","unstructured":"Yu K, Lin YQ, Lafferty J (2011) Learning image representations from the pixel level via hierarchical sparse coding. In: IEEE conference on computer vision and pattern recognition, Colorado Springs, 2011, pp 1713\u20131720","DOI":"10.1109\/CVPR.2011.5995732"},{"issue":"4","key":"9822_CR55","doi-asserted-by":"crossref","first-page":"953","DOI":"10.1109\/TII.2012.2205932","volume":"8","author":"J Zhao","year":"2012","unstructured":"Zhao J, Liu QL, Pedrycz W, Li DX (2012) Effective noise estimation-based online prediction for byproduct gas system in steel industry. IEEE Trans Ind Inf 8(4):953\u2013963","journal-title":"IEEE Trans Ind Inf"},{"issue":"2","key":"9822_CR56","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1109\/TCYB.2015.2445918","volume":"46","author":"J Zhao","year":"2015","unstructured":"Zhao J, Han ZY, Pedrycz W, Wang W (2015) Granular model of long-term prediction for energy system in steel industry. IEEE Trans Cybern 46(2):388\u2013400","journal-title":"IEEE Trans Cybern"},{"key":"9822_CR57","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/j.dsp.2015.09.015","volume":"48","author":"YF Zheng","year":"2016","unstructured":"Zheng YF, Wang SY, Feng JC, Tse CK (2016) A modified quantized kernel least mean square algorithm for prediction of chaotic time series. Dig Signal Proc 48:130\u2013136","journal-title":"Dig Signal Proc"}],"container-title":["Artificial Intelligence Review"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-020-09822-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10462-020-09822-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-020-09822-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,2,21]],"date-time":"2021-02-21T00:41:03Z","timestamp":1613868063000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10462-020-09822-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,2,22]]},"references-count":57,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2020,10]]}},"alternative-id":["9822"],"URL":"https:\/\/doi.org\/10.1007\/s10462-020-09822-9","relation":{},"ISSN":["0269-2821","1573-7462"],"issn-type":[{"value":"0269-2821","type":"print"},{"value":"1573-7462","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,2,22]]},"assertion":[{"value":"22 February 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}