{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T01:55:02Z","timestamp":1775181302486,"version":"3.50.1"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"23","license":[{"start":{"date-parts":[[2021,6,24]],"date-time":"2021-06-24T00:00:00Z","timestamp":1624492800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,6,24]],"date-time":"2021-06-24T00:00:00Z","timestamp":1624492800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2021,12]]},"DOI":"10.1007\/s00521-021-06232-y","type":"journal-article","created":{"date-parts":[[2021,6,24]],"date-time":"2021-06-24T19:02:30Z","timestamp":1624561350000},"page":"16329-16343","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":61,"title":["Deep learning models for forecasting aviation demand time series"],"prefix":"10.1007","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9964-4134","authenticated-orcid":false,"given":"Andreas","family":"Kanavos","sequence":"first","affiliation":[]},{"given":"Fotios","family":"Kounelis","sequence":"additional","affiliation":[]},{"given":"Lazaros","family":"Iliadis","sequence":"additional","affiliation":[]},{"given":"Christos","family":"Makris","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,6,24]]},"reference":[{"key":"6232_CR1","unstructured":"Andreoni A, Postorino MN (2006) A multivariate arima model to forecast air transport demand. Assoc Eur Transp Contribut pp 1\u201314"},{"key":"6232_CR2","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1016\/j.jtrangeo.2015.02.001","volume":"43","author":"D Baker","year":"2015","unstructured":"Baker D, Merkert R, Kamruzzaman M (2015) Regional aviation and economic growth: cointegration and causality analysis in australia. J Trans Geograp 43:140\u2013150","journal-title":"J Trans Geograp"},{"key":"6232_CR3","doi-asserted-by":"crossref","unstructured":"Bao Y, Xiong T , Hu Z (2012) Forecasting air passenger traffic by support vector machines with ensemble empirical mode decomposition and slope-based method. Dis Dynam Nat Soc","DOI":"10.1155\/2012\/431512"},{"key":"6232_CR4","unstructured":"Box GEP, Jenkins GM, Reinsel GC (2011) Time series analysis: Forecasting and Control, vol 734. John Wiley & Sons"},{"issue":"3","key":"6232_CR5","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1007\/BF02294361","volume":"52","author":"H Bozdogan","year":"1987","unstructured":"Bozdogan H (1987) Model selection and akaike\u2019s information criterion (aic): the general theory and its analytical extensions. Psychometrika 52(3):345\u2013370","journal-title":"Psychometrika"},{"key":"6232_CR6","doi-asserted-by":"crossref","unstructured":"Ciresan DC, Meier U, Schmidhuber J (2012) Multi-column deep neural networks for image classification. In: IEEE Conference on Computer Vision and Pattern Recognition, pp 3642\u20133649","DOI":"10.1109\/CVPR.2012.6248110"},{"key":"6232_CR7","volume-title":"Cross-scene trajectory level intention inference using gaussian process regression and naive registration","author":"D Das","year":"2018","unstructured":"Das D, Lee CSG (2018) Cross-scene trajectory level intention inference using gaussian process regression and naive registration. Purdue University, Tech. rep"},{"key":"6232_CR8","unstructured":"Diebold FX (1998) Elements of forecasting. South-Western College Pub"},{"key":"6232_CR9","first-page":"2121","volume":"12","author":"JC Duchi","year":"2011","unstructured":"Duchi JC, Hazan E, Singer Y (2011) Adaptive subgradient methods for online learning and stochastic optimization. J Mach Learn Res 12:2121\u20132159","journal-title":"J Mach Learn Res"},{"issue":"101","key":"6232_CR10","first-page":"555","volume":"65","author":"AA Ewees","year":"2020","unstructured":"Ewees AA, Elaziz MA, Alameer Z, Ye H, Jianhua Z (2020) Improving multilayer perceptron neural network using chaotic grasshopper optimization algorithm to forecast iron ore price volatility. Resour Policy 65(101):555","journal-title":"Resour Policy"},{"issue":"2","key":"6232_CR11","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1080\/01944360508976688","volume":"71","author":"B Flyvbjerg","year":"2005","unstructured":"Flyvbjerg B, Holm MKS, Buhl SL (2005) How (in)accurate are demand forecasts in public works projects?: the case of transportation. J Am Plan Assoc 71(2):131\u2013146","journal-title":"J Am Plan Assoc"},{"key":"6232_CR12","volume-title":"Neural networks: algorithms, applications, and programming techniques","author":"JA Freeman","year":"1991","unstructured":"Freeman JA, Skapura DM (1991) Neural networks: algorithms, applications, and programming techniques. Addison-Wesley, Computation and Neural Systems Series"},{"key":"6232_CR13","unstructured":"Goel H, Melnyk I, Oza N, Matthews B, Banerjee A (2016) Multivariate aviation time series modeling: Vars vs. lstms. Unpublished manuscript Retrieved from https:\/\/www semanticscholar org\/paper\/Multivariate-Aviation-Time-Series-Modeling"},{"key":"6232_CR14","doi-asserted-by":"crossref","unstructured":"Gourgaris P, Kanavos A, Makris C, Perrakis G (2015) Review-based entity-ranking refinement. In: 11th International Conference on Web Information Systems and Technologies (WEBIST), pp 402\u2013410","DOI":"10.5220\/0005428604020410"},{"key":"6232_CR15","unstructured":"Gulli A, Pal S (2017) Deep learning with keras. Packt Publishing Ltd"},{"issue":"7","key":"6232_CR16","doi-asserted-by":"publisher","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","volume":"18","author":"GE Hinton","year":"2006","unstructured":"Hinton GE, Osindero S, Teh YW (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527\u20131554","journal-title":"Neural Comput"},{"key":"6232_CR17","unstructured":"Ioffe S, Szegedy C (2015) Batch normalization: Accelerating deep network training by reducing internal covariate shift. CoRR abs\/1502.03167"},{"issue":"101","key":"6232_CR18","first-page":"744","volume":"83","author":"F Jin","year":"2020","unstructured":"Jin F, Li Y, Sun S, Li H (2020) Forecasting air passenger demand with a new hybrid ensemble approach. J Air Trans Manag 83(101):744","journal-title":"J Air Trans Manag"},{"key":"6232_CR19","doi-asserted-by":"crossref","unstructured":"Kanavos A, Kafeza E, Makris C (2015) Can we rank emotions? A brand love ranking system for emotional terms. In: 2015 IEEE International Congress on Big Data, pp 71\u201378","DOI":"10.1109\/BigDataCongress.2015.20"},{"key":"6232_CR20","doi-asserted-by":"publisher","first-page":"2625","DOI":"10.1080\/03610918.2018.1458138","volume":"48","author":"M Khashei","year":"2019","unstructured":"Khashei M, Hajirahimi Z (2019) A comparative study of series arima\/mlp hybrid models for stock price forecasting. Commun Statistics - Simul Comput 48:2625\u20132640","journal-title":"Commun Statistics - Simul Comput"},{"key":"6232_CR21","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1016\/j.autcon.2016.06.009","volume":"70","author":"S Kim","year":"2016","unstructured":"Kim S, Shin DH (2016) Forecasting short-term air passenger demand using big data from search engine queries. Autom Construc 70:98\u2013108","journal-title":"Autom Construc"},{"key":"6232_CR22","unstructured":"Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations (ICLR)"},{"key":"6232_CR23","unstructured":"Krishnamurthy G, Majumder N, Poria S, Cambria E (2018) A deep learning approach for multimodal deception detection. CoRR abs\/1803.00344"},{"key":"6232_CR24","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: 26th Annual Conference on Neural Information Processing Systems (NIPS), pp 1106\u20131114"},{"issue":"5","key":"6232_CR25","doi-asserted-by":"publisher","first-page":"945","DOI":"10.1007\/s13198-019-00825-6","volume":"10","author":"C Li","year":"2019","unstructured":"Li C (2019) Combined forecasting of civil aviation passenger volume based on ARIMA-REGRESSION. Int J Syst Assurance Eng Manag 10(5):945\u2013952","journal-title":"Int J Syst Assurance Eng Manag"},{"key":"6232_CR26","unstructured":"Li J, Huang P, Yang Y, Peng Q (2019) Passenger flow prediction of high speed railway based on lstm deep neural network. In: 8th International Conference on Railway Operations Modelling and Analysis (ICROMA), pp 723\u2013739"},{"key":"6232_CR27","doi-asserted-by":"crossref","unstructured":"Maheshwari A, Davendralingam N, DeLaurentis DA (2018) A comparative study of machine learning techniques for aviation applications. In: 2018 Aviation Technology, Integration, and Operations Conference, p 3980","DOI":"10.2514\/6.2018-3980"},{"issue":"13","key":"6232_CR28","doi-asserted-by":"publisher","first-page":"2895","DOI":"10.3390\/s19132895","volume":"19","author":"Y Mao","year":"2019","unstructured":"Mao Y, Zhang J, Qi H, Wang L (2019) DNN-MVL: dnn-multi-view-learning-based recover block missing data in a dam safety monitoring system. Sensors 19(13):2895","journal-title":"Sensors"},{"key":"6232_CR29","doi-asserted-by":"crossref","unstructured":"Ming W, Bao Y, Hu Z, Xiong T (2014) Multistep-ahead air passengers traffic prediction with hybrid arima-svms models. Sci World J","DOI":"10.1155\/2014\/567246"},{"key":"6232_CR30","doi-asserted-by":"crossref","unstructured":"de O Junior DS, de Oliveira JFL, de Mattos Neto PSG, (2019) An intelligent hybridization of ARIMA with machine learning models for time series forecasting. Knowl Based Syst 175:72\u201386","DOI":"10.1016\/j.knosys.2019.03.011"},{"key":"6232_CR31","doi-asserted-by":"crossref","unstructured":"Ozozen A, Kayakutlu G, Ketterer M, Kayalica O (2016) A combined seasonal arima and ann model for improved results in electricity spot price forecasting: Case study in turkey. In: International Conference on Management of Engineering and Technology (PICMET), pp 2681\u20132690","DOI":"10.1109\/PICMET.2016.7806831"},{"key":"6232_CR32","doi-asserted-by":"publisher","DOI":"10.1002\/9781119432036","volume-title":"Linear models and time-series analysis: regression","author":"MS Paolella","year":"2018","unstructured":"Paolella MS (2018) Linear models and time-series analysis: Regression. John Wiley & Sons, ANOVA, ARMA and GARCH"},{"issue":"10","key":"6232_CR33","doi-asserted-by":"publisher","first-page":"157","DOI":"10.3390\/a11100157","volume":"11","author":"A Savvopoulos","year":"2018","unstructured":"Savvopoulos A, Kanavos A, Mylonas P, Sioutas S (2018) LSTM accelerator for convolutional object identification. Algorithms 11(10):157","journal-title":"Algorithms"},{"key":"6232_CR34","doi-asserted-by":"crossref","unstructured":"Schultz M, Reitmann S (2018) Prediction of aircraft boarding time using lstm network. In: Winter Simulation Conference (WSC), pp 2330\u20132341","DOI":"10.1109\/WSC.2018.8632532"},{"issue":"2","key":"6232_CR35","doi-asserted-by":"publisher","first-page":"90","DOI":"10.3846\/16487788.2015.1054157","volume":"19","author":"P Srisaeng","year":"2015","unstructured":"Srisaeng P, Baxter GS, Wild G (2015) Forecasting demand for low cost carriers in australia using an artificial neural network approach. Aviation 19(2):90\u2013103","journal-title":"Aviation"},{"key":"6232_CR36","doi-asserted-by":"publisher","first-page":"400","DOI":"10.1016\/j.tre.2019.06.016","volume":"128","author":"DY Suh","year":"2019","unstructured":"Suh DY, Ryerson MS (2019) Forecast to grow: Aviation demand forecasting in an era of demand uncertainty and optimism bias. Transp Res Part E: Logist Trans Rev 128:400\u2013416","journal-title":"Transp Res Part E: Logist Trans Rev"},{"key":"6232_CR37","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1016\/j.jairtraman.2019.04.005","volume":"78","author":"S Sun","year":"2019","unstructured":"Sun S, Lu H, Tsui KL, Wang S (2019) Nonlinear vector auto-regression neural network for forecasting air passenger flow. J Air Trans Manag 78:54\u201362","journal-title":"J Air Trans Manag"},{"key":"6232_CR38","unstructured":"Sutskever I, Martens J, Dahl GE, Hinton GE (2013) On the importance of initialization and momentum in deep learning. In: International Conference on Machine Learning (ICML), pp 1139\u20131147"},{"key":"6232_CR39","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1016\/j.tourman.2013.10.008","volume":"42","author":"WHK Tsui","year":"2014","unstructured":"Tsui WHK, Balli HO, Gilbey A, Gow H (2014) Forecasting of hong kong airport\u2019s passenger throughput. Tourism Manag 42:62\u201376","journal-title":"Tourism Manag"},{"key":"6232_CR40","unstructured":"Waciko KJ, B I, (2020) Sarima-elm hybrid model versus sarima-mlp hybrid model. Int J Statist Appl Math 5(2):01\u201308"},{"key":"6232_CR41","unstructured":"Wu N, Green B, Ben X, O\u2019Banion S (2020) Deep transformer models for time series forecasting: The influenza prevalence case. CoRR abs\/2001.08317"},{"key":"6232_CR42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jairtraman.2014.03.004","volume":"39","author":"Y Xiao","year":"2014","unstructured":"Xiao Y, Liu JJ, Hu Y, Wang Y, Lai KK, Wang S (2014) A neuro-fuzzy combination model based on singular spectrum analysis for air transport demand forecasting. J Air Trans Manag 39:1\u201311","journal-title":"J Air Trans Manag"},{"key":"6232_CR43","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1016\/j.tre.2018.12.005","volume":"122","author":"S Xu","year":"2019","unstructured":"Xu S, Chan HK, Zhang T (2019) Forecasting the demand of the aviation industry using hybrid time series sarima-svr approach. Trans Res Part E: Logist Trans Rev 122:169\u2013180","journal-title":"Trans Res Part E: Logist Trans Rev"},{"key":"6232_CR44","unstructured":"Zaccone G (2016) Getting started with tensorFlow. Packt Publishing Ltd"},{"issue":"1","key":"6232_CR45","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/S0169-2070(97)00044-7","volume":"14","author":"G Zhang","year":"1998","unstructured":"Zhang G, Patuwo BE, Hu MY (1998) Forecasting with artificial neural networks: The state of the art. Int J Forecast 14(1):35\u201362","journal-title":"Int J Forecast"},{"key":"6232_CR46","doi-asserted-by":"crossref","unstructured":"Zhang J, Zheng Y, Qi D, Li R, Yi X (2016) Dnn-based prediction model for spatio-temporal data. In: 24th ACM SIG\/SPATIAL International Conference on Advances in Geographic Information Systems (GIS), pp 92:1\u201392:4","DOI":"10.1145\/2996913.2997016"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06232-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-021-06232-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06232-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,11,3]],"date-time":"2021-11-03T18:10:46Z","timestamp":1635963046000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-021-06232-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,24]]},"references-count":46,"journal-issue":{"issue":"23","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["6232"],"URL":"https:\/\/doi.org\/10.1007\/s00521-021-06232-y","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,24]]},"assertion":[{"value":"29 January 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 June 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 June 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}