{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T07:06:41Z","timestamp":1768806401522,"version":"3.49.0"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T00:00:00Z","timestamp":1743120000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T00:00:00Z","timestamp":1743120000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Pattern Anal Applic"],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1007\/s10044-025-01451-8","type":"journal-article","created":{"date-parts":[[2025,3,31]],"date-time":"2025-03-31T04:56:18Z","timestamp":1743396978000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Dual stream deep attention networks for annual population projection"],"prefix":"10.1007","volume":"28","author":[{"given":"Adnan","family":"Hussain","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hikmat","family":"Yar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Noman","family":"Khan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zulfiqar Ahmad","family":"Khan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Min Je","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sung Wook","family":"Baik","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,3,28]]},"reference":[{"key":"1451_CR1","unstructured":"Desa U (2022) World population prospects 2022: Summary of results. United Nations Department of Economic and Social Affairs, Population Division, Tech. Rep. UN DESA\/POP\/2022\/TR, 2022(3)"},{"key":"1451_CR2","unstructured":"Vespa JE, Armstrong DM, Medina L (2018) Demographic turning points for the united States: population projections for 2020 to 2060. US Department of Commerce, Economics and Statistics Administration, US \u2026"},{"key":"1451_CR3","unstructured":"Research NIoPaSS Population projections for Japan (2016\u20132065). 08\/01\/2024]; Available from: https:\/\/www.ipss.go.jp\/pp-zenkoku\/e\/zenkoku_e2017\/pp_zenkoku2017e_gaiyou.html. Accessed on 5 Nov 2024"},{"issue":"1","key":"1451_CR4","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1016\/j.ijforecast.2021.09.001","volume":"39","author":"AE Raftery","year":"2023","unstructured":"Raftery AE, \u0160ev\u010d\u00edkov\u00e1 H (2023) Probabilistic population forecasting: short to very long-term. Int J Forecast 39(1):73\u201397","journal-title":"Int J Forecast"},{"key":"1451_CR5","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1016\/j.knosys.2016.03.006","volume":"100","author":"MYH Al-Shamri","year":"2016","unstructured":"Al-Shamri MYH (2016) User profiling approaches for demographic recommender systems. Knowl Based Syst 100:175\u2013187","journal-title":"Knowl Based Syst"},{"key":"1451_CR6","doi-asserted-by":"crossref","unstructured":"Shumway RH et al (2017) ARIMA models. Time series analysis and its applications: with R examples, pp. 75\u2013163","DOI":"10.1007\/978-3-319-52452-8_3"},{"key":"1451_CR7","doi-asserted-by":"crossref","unstructured":"Lazri M et al (2023) Comparison between SVR and SVM in rainfall estimation from remote sensing data. In:  International Conference on Information Science and Applications. Springer","DOI":"10.1007\/978-981-99-6984-5_19"},{"key":"1451_CR8","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1016\/j.measurement.2017.02.007","volume":"103","author":"Y Yaslan","year":"2017","unstructured":"Yaslan Y, Bican B (2017) Empirical mode decomposition based denoising method with support vector regression for time series prediction: a case study for electricity load forecasting. Measurement 103:52\u201361","journal-title":"Measurement"},{"key":"1451_CR9","doi-asserted-by":"publisher","first-page":"122339","DOI":"10.1016\/j.apenergy.2023.122339","volume":"356","author":"ZA Khan","year":"2024","unstructured":"Khan ZA et al (2024) Dual sequence prediction model for efficient energy management in micro-grid. Appl Energy 356:122339","journal-title":"Appl Energy"},{"key":"1451_CR10","unstructured":"Wu J et al (2023) How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression? arXiv preprint arXiv:2310.08391"},{"key":"1451_CR11","doi-asserted-by":"publisher","first-page":"115222","DOI":"10.1016\/j.measurement.2024.115222","volume":"237","author":"H Wu","year":"2024","unstructured":"Wu H, Du P, Heng J (2024) Gated Convolution with attention mechanism under variational mode decomposition for daily rainfall forecasting. Measurement 237:115222","journal-title":"Measurement"},{"key":"1451_CR12","doi-asserted-by":"publisher","first-page":"115405","DOI":"10.1016\/j.measurement.2024.115405","volume":"239","author":"Z Su","year":"2025","unstructured":"Su Z et al (2025) Improving ultra-short-term photovoltaic power forecasting using advanced deep-learning approach. Measurement 239:115405","journal-title":"Measurement"},{"key":"1451_CR13","unstructured":"Hajirahimova MS, SAliyeva A (2023) Development of a prediction model on demographic indicators based on machine learning methods. Azerbaijan Example"},{"key":"1451_CR14","first-page":"545","volume":"12","author":"A Abbasov","year":"2003","unstructured":"Abbasov A, Mamedova M (2003) Application of fuzzy time series to population forecasting. Vienna Univ Technol 12:545\u2013552","journal-title":"Vienna Univ Technol"},{"issue":"2","key":"1451_CR15","doi-asserted-by":"publisher","first-page":"18","DOI":"10.15587\/1729-4061.2019.178440","volume":"5","author":"Z Jabrayilova","year":"2019","unstructured":"Jabrayilova Z (2019) Development of intelligent demographic forecasting system. Eastern-European J Enterp Technol 5(2):18\u201325","journal-title":"Eastern-European J Enterp Technol"},{"issue":"12","key":"1451_CR16","doi-asserted-by":"publisher","first-page":"3851","DOI":"10.1007\/s00521-016-2261-4","volume":"28","author":"P Singh","year":"2017","unstructured":"Singh P (2017) High-order fuzzy-neuro-entropy integration-based expert system for time series forecasting. Neural Comput Appl 28(12):3851\u20133868","journal-title":"Neural Comput Appl"},{"issue":"12","key":"1451_CR17","first-page":"96","volume":"19","author":"MM Otoom","year":"2019","unstructured":"Otoom MM et al (2019) Comparative analysis of different machine learning models for estimating the population growth rate in data-limited area. IJCSNS 19(12):96","journal-title":"IJCSNS"},{"key":"1451_CR18","unstructured":"\u015eahinarslan FV, Tekin AT, \u00c7ebi F (2019) Machine learning algorithms to forecast population: Turkey example. \u0130stanbul Technical University, Internat\u0131onal Eng\u0131neer\u0131ng And Technology Management"},{"issue":"1","key":"1451_CR19","first-page":"220","volume":"21","author":"MM Otoom","year":"2021","unstructured":"Otoom MM (2021) Comparing the performance of 17 machine learning models in predicting human population growth of countries. Int J Comput Sci Netw Secur 21(1):220\u2013225","journal-title":"Int J Comput Sci Netw Secur"},{"issue":"2","key":"1451_CR20","doi-asserted-by":"publisher","first-page":"366","DOI":"10.1177\/23998083231178817","volume":"51","author":"I Grossman","year":"2024","unstructured":"Grossman I et al (2024) Development and evaluation of probabilistic forecasting methods for small area populations. Environ Plann B Urban Anal City Sci 51(2):366\u2013383","journal-title":"Environ Plann B Urban Anal City Sci"},{"key":"1451_CR21","doi-asserted-by":"publisher","first-page":"101658","DOI":"10.1016\/j.seps.2023.101658","volume":"88","author":"I Grossman","year":"2023","unstructured":"Grossman I, Wilson T, Temple J (2023) Forecasting small area populations with long short-term memory networks. Socio-Econ Plann Sci 88:101658","journal-title":"Socio-Econ Plann Sci"},{"key":"1451_CR22","doi-asserted-by":"publisher","first-page":"120644","DOI":"10.1016\/j.jclepro.2020.120644","volume":"256","author":"S Zhao","year":"2020","unstructured":"Zhao S et al (2020) China\u2019s population spatialization based on three machine learning models. J Clean Prod 256:120644","journal-title":"J Clean Prod"},{"key":"1451_CR23","doi-asserted-by":"publisher","first-page":"103962","DOI":"10.1016\/j.trc.2022.103962","volume":"146","author":"S Hu","year":"2023","unstructured":"Hu S, Xiong C (2023) High-dimensional population inflow time series forecasting via an interpretable hierarchical transformer. Transp Res Part C Emerg Technol 146:103962","journal-title":"Transp Res Part C Emerg Technol"},{"key":"1451_CR24","first-page":"103731","volume":"128","author":"S Doda","year":"2024","unstructured":"Doda S et al (2024) Interpretable deep learning for consistent large-scale urban population Estimation using Earth observation data. Int J Appl Earth Obs Geoinf 128:103731","journal-title":"Int J Appl Earth Obs Geoinf"},{"key":"1451_CR25","unstructured":"Statistics AB (2017) o. ERP by SA2 and above (ASGS 2011), 1991 to 2016. ; Available from: https:\/\/demographic-datasets-network.github.io\/. Accessed on 17 Aug 2024"},{"key":"1451_CR26","unstructured":"Zealand SN (2020) Subnational population estimates (RC, SA2), by age and sex, at 30 June 1996\u20132020 (2020 boundaries), Statistics New Zealand NZ.Stat. ; Available from: https:\/\/demographic-datasets-network.github.io\/. Accessed on 17 Aug 2024"},{"key":"1451_CR27","unstructured":"KOSIS, Korean Statistical Information Service (2024)"},{"key":"1451_CR28","first-page":"102337","volume":"53","author":"ZA Khan","year":"2022","unstructured":"Khan ZA et al (2022) Efficient short-term electricity load forecasting for effective energy management. Sustain Energy Technol Assess 53:102337","journal-title":"Sustain Energy Technol Assess"},{"issue":"4","key":"1451_CR29","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1002\/psp.1847","volume":"21","author":"T Wilson","year":"2015","unstructured":"Wilson T (2015) New evaluations of simple models for small area population forecasts. Popul Space Place 21(4):335\u2013353","journal-title":"Popul Space Place"},{"issue":"1","key":"1451_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s40980-021-00103-9","volume":"10","author":"T Wilson","year":"2022","unstructured":"Wilson T, Grossman I (2022) Evaluating alternative implementations of the Hamilton-Perry model for small area population forecasts: the case of Australia. Spat Demography 10(1):1\u201331","journal-title":"Spat Demography"},{"key":"1451_CR31","doi-asserted-by":"publisher","first-page":"101806","DOI":"10.1016\/j.compenvurbsys.2022.101806","volume":"95","author":"I Grossman","year":"2022","unstructured":"Grossman I et al (2022) Can machine learning improve small area population forecasts? A forecast combination approach. Comput Environ Urban Syst 95:101806","journal-title":"Comput Environ Urban Syst"},{"issue":"1","key":"1451_CR32","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1016\/j.ijforecast.2021.09.005","volume":"39","author":"T Wilson","year":"2023","unstructured":"Wilson T, Grossman I, Temple J (2023) Evaluation of the best M4 competition methods for small area population forecasting. Int J Forecast 39(1):110\u2013122","journal-title":"Int J Forecast"}],"container-title":["Pattern Analysis and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10044-025-01451-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10044-025-01451-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10044-025-01451-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T16:40:20Z","timestamp":1751474420000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10044-025-01451-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,28]]},"references-count":32,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["1451"],"URL":"https:\/\/doi.org\/10.1007\/s10044-025-01451-8","relation":{},"ISSN":["1433-7541","1433-755X"],"issn-type":[{"value":"1433-7541","type":"print"},{"value":"1433-755X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,28]]},"assertion":[{"value":"27 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 March 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 March 2025","order":3,"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"}}],"article-number":"71"}}