{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T10:02:18Z","timestamp":1760608938094},"reference-count":23,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2016,11,12]],"date-time":"2016-11-12T00:00:00Z","timestamp":1478908800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"name":"Program for Changjiang Scholars and Innovative Research Team in University of Ministry of Education of China","award":["IRT1017"],"award-info":[{"award-number":["IRT1017"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2018,6]]},"DOI":"10.1007\/s00521-016-2698-5","type":"journal-article","created":{"date-parts":[[2016,11,12]],"date-time":"2016-11-12T18:06:31Z","timestamp":1478973991000},"page":"1535-1543","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Recursive Bayesian echo state network with an adaptive inflation factor for temperature prediction"],"prefix":"10.1007","volume":"29","author":[{"given":"Biaobing","family":"Huang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guihe","family":"Qin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rui","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiong","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alireza","family":"Shahriari","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2016,11,12]]},"reference":[{"key":"2698_CR1","first-page":"613","volume":"2","author":"M Hayati","year":"2007","unstructured":"Hayati M, Mohebi Z (2007) Temperature forecasting based on neural network approach. World Appl Sci J 2:613\u2013620","journal-title":"World Appl Sci J"},{"key":"2698_CR2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2013\/489350","volume":"3","author":"AP Kamoutsis","year":"2013","unstructured":"Kamoutsis AP, Matsoukis AS, Chronopoulos KI (2013) Air temperature estimation by using neural network models in the Grater Athens Area, Greece. ISRN Meteorol 3:1\u20137","journal-title":"ISRN Meteorol"},{"key":"2698_CR3","doi-asserted-by":"crossref","first-page":"2783","DOI":"10.1109\/TGRS.2003.817225","volume":"41","author":"NR Pal","year":"2003","unstructured":"Pal NR, Pal S, Das J, Majumdar K (2003) SOFM-MLP: a hybrid neural network for atmospheric temperature prediction. IEEE Trans Geosci Remote Sens 41:2783\u20132791","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"2698_CR4","first-page":"37","volume":"1","author":"SS De","year":"2009","unstructured":"De SS, Debnath A (2009) Artificial neural network based prediction of maximum and minimum temperature in the summer monsoon months over India. Appl Phys Res 1:37\u201344","journal-title":"Appl Phys Res"},{"key":"2698_CR5","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1002\/met.83","volume":"15","author":"B Ustaoglu","year":"2008","unstructured":"Ustaoglu B, Cigizoglu HK, Karaca M (2008) Forecast of daily mean, maximum and minimum temperature time series by three artificial neural network methods. Meteorol Appl 15:431\u2013445","journal-title":"Meteorol Appl"},{"key":"2698_CR6","doi-asserted-by":"crossref","first-page":"283","DOI":"10.54302\/mausam.v63i2.1423","volume":"63","author":"Piyush Joshi","year":"2012","unstructured":"Joshi Piyush, Ganju A (2012) Maximum and minimum temperature prediction over western Himalaya using artificial neural network. Mausam 63:283\u2013290","journal-title":"Mausam"},{"key":"2698_CR7","first-page":"1","volume":"3","author":"Pankaj Kumar","year":"2012","unstructured":"Kumar Pankaj (2012) Minimum weekly temperature forecasting using ANFIS. Comput Eng Intell Syst 3:1\u20136","journal-title":"Comput Eng Intell Syst"},{"key":"2698_CR8","first-page":"2029","volume":"2","author":"A Saxena","year":"2013","unstructured":"Saxena A, Verma N, Tripathi KC (2013) A review study of weather forecasting using artificial neural network approach. Int J Eng Res Technol 2:2029\u20132035","journal-title":"Int J Eng Res Technol"},{"issue":"5","key":"2698_CR9","doi-asserted-by":"crossref","first-page":"2253","DOI":"10.1016\/j.asoc.2013.02.003","volume":"13","author":"S Venkadesh","year":"2013","unstructured":"Venkadesh S, Hoogenboom G, Potter W, McClendon R (2013) A genetic algorithm to refine input data selection for air temperature prediction using artificial neural networks. Appl Soft Comput 13(5):2253\u20132260","journal-title":"Appl Soft Comput"},{"issue":"1","key":"2698_CR10","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1002\/joc.3676","volume":"34","author":"O Kisi","year":"2014","unstructured":"Kisi O, Shiri J (2014) Prediction of long-term monthly air temperature using geographical inputs. Int J Climatol 34(1):179\u2013186","journal-title":"Int J Climatol"},{"issue":"1","key":"2698_CR11","first-page":"25","volume":"8","author":"BS Karthika","year":"2016","unstructured":"Karthika BS, Deka PC (2016) Modeling of air temperature using ANFIS by wavelet refined parameters. Int J Intell Syst Appl 8(1):25\u201334","journal-title":"Int J Intell Syst Appl"},{"key":"2698_CR12","first-page":"1793","volume":"1","author":"Y Radhika","year":"2009","unstructured":"Radhika Y, Shashi M (2009) Atmospheric temperature prediction using support vector machines. Int J Comput Theory Eng 1:1793\u20138201","journal-title":"Int J Comput Theory Eng"},{"key":"2698_CR13","doi-asserted-by":"crossref","unstructured":"Karevan Z, Mehrkanoon S, Suykens JA (2015) Black-box modeling for temperature prediction in weather forecasting. In: 2015 International Joint Conference on Neural Networks (IJCNN). pp 1\u20138","DOI":"10.1109\/IJCNN.2015.7280671"},{"key":"2698_CR14","unstructured":"Karevan Z, Suykens J (2016) Spatio-temporal feature selection for black-box weather forecasting. In: Proceedings of the 24th European symposium on artificial neural networks, computational intelligence and machine learning. pp 611\u2013616"},{"key":"2698_CR15","doi-asserted-by":"crossref","first-page":"578","DOI":"10.1002\/met.1379","volume":"21","author":"TC Mills","year":"2014","unstructured":"Mills TC (2014) Time series modelling of temperatures: an example from Kefalonia. Meteorol Appl 21:578\u2013584","journal-title":"Meteorol Appl"},{"issue":"3","key":"2698_CR16","doi-asserted-by":"crossref","first-page":"137","DOI":"10.3923\/rjes.2015.137.151","volume":"9","author":"MR Kaloop","year":"2015","unstructured":"Kaloop MR, Abdelaal MM, El Shambaky HT (2015) Optimum ARX model prediction for monthly air temperature changes in delta, Egypt. Res J Environ Sci 9(3):137\u2013151","journal-title":"Res J Environ Sci"},{"issue":"4","key":"2698_CR17","doi-asserted-by":"crossref","first-page":"2121","DOI":"10.1007\/s12517-014-1278-1","volume":"8","author":"A Shirvani","year":"2015","unstructured":"Shirvani A, Nazemosadat SMJ, Kahya E (2015) Analyses of the Persian Gulf sea surface temperature: prediction and detection of climate change signals. Arab J Geosci 8(4):2121\u20132130","journal-title":"Arab J Geosci"},{"key":"2698_CR18","volume-title":"Statistical forecasting how fast will future warming be?","author":"TC Mills","year":"2016","unstructured":"Mills TC (2016) Statistical forecasting how fast will future warming be? Global Warming Policy Foundation, London"},{"key":"2698_CR19","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1126\/science.1091277","volume":"304","author":"H Jaeger","year":"2004","unstructured":"Jaeger H, Haas H (2004) Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication. Science 304:78\u201380","journal-title":"Science"},{"key":"2698_CR20","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1002\/joc.3370120402","volume":"12","author":"DE Parker","year":"1992","unstructured":"Parker DE, Legg TP, Folland CK (1992) A new daily central England temperature series. Int J Clim 12:317\u2013342","journal-title":"Int J Clim"},{"key":"2698_CR21","unstructured":"Schrauwen B, Verstraeten D, Van Campenhout J (2007) An overview of reservoir computing: theory, applications and implementations. In: Proceedings of the 15th European Symposium on Artificial Neural Networks"},{"key":"2698_CR22","unstructured":"S\u00e4rkk\u00e4 S (2012) Bayesian estimation of time-varying systems: discrete-time systems. In: Written material for the course. Aalto University, School of Science and Technology, pp 21\u201324"},{"key":"2698_CR23","doi-asserted-by":"crossref","unstructured":"Luko\u0161evi\u010dius M (2012) A practical guide to applying echo state networks. In: Neural networks: tricks of the Trade. Springer, Berlin, pp 659\u2013686","DOI":"10.1007\/978-3-642-35289-8_36"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-016-2698-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-016-2698-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-016-2698-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,13]],"date-time":"2022-07-13T05:52:24Z","timestamp":1657691544000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-016-2698-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,11,12]]},"references-count":23,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2018,6]]}},"alternative-id":["2698"],"URL":"https:\/\/doi.org\/10.1007\/s00521-016-2698-5","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,11,12]]}}}