{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T08:53:37Z","timestamp":1773219217079,"version":"3.50.1"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T00:00:00Z","timestamp":1773100800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T00:00:00Z","timestamp":1773100800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100010249","name":"Youth Science Foundation of Lanzhou Jiaotong University","doi-asserted-by":"publisher","award":["1200061320"],"award-info":[{"award-number":["1200061320"]}],"id":[{"id":"10.13039\/501100010249","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72161022"],"award-info":[{"award-number":["72161022"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018554","name":"Science and Technology Program of Gansu Province","doi-asserted-by":"publisher","award":["24JRRA847"],"award-info":[{"award-number":["24JRRA847"]}],"id":[{"id":"10.13039\/501100018554","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Data Sci Anal"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1007\/s41060-026-01074-0","type":"journal-article","created":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T10:59:30Z","timestamp":1773140370000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A two-layer multivariate decomposition framework for spatiotemporal tourism demand forecasting based on Internet search and city synergy factors"],"prefix":"10.1007","volume":"22","author":[{"given":"Haina","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenzheng","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Shi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongtao","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shaolong","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,3,10]]},"reference":[{"key":"1074_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.tourman.2025.105138","volume":"109","author":"M Hu","year":"2025","unstructured":"Hu, M., Liang, W., Qiu, R.T.R., Wu, D.C.: Tourism demand forecasting using compound pattern recognition. Tour. Manage. 109, 105138 (2025). https:\/\/doi.org\/10.1016\/j.tourman.2025.105138","journal-title":"Tour. Manage."},{"issue":"3","key":"1074_CR2","doi-asserted-by":"publisher","first-page":"1653","DOI":"10.1016\/j.annals.2012.05.023","volume":"39","author":"H Song","year":"2012","unstructured":"Song, H., Dwyer, L., Li, G., Cao, Z.: Tourism economics research: A review and assessment. Ann. Tour. Res. 39(3), 1653\u20131682 (2012). https:\/\/doi.org\/10.1016\/j.annals.2012.05.023","journal-title":"Ann. Tour. Res."},{"key":"1074_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.annals.2020.102937","volume":"83","author":"X Jiao","year":"2020","unstructured":"Jiao, X., Li, G., Chen, J.L.: Forecasting international tourism demand: a local spatiotemporal model. Ann. Tour. Res. 83, 102937 (2020). https:\/\/doi.org\/10.1016\/j.annals.2020.102937","journal-title":"Ann. Tour. Res."},{"key":"1074_CR4","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1016\/j.tourman.2017.10.014","volume":"66","author":"T Dergiades","year":"2018","unstructured":"Dergiades, T., Mavragani, E., Pan, B.: Google trends and tourists\u2019 arrivals: Emerging biases and proposed corrections. Tour. Manage. 66, 108\u2013120 (2018). https:\/\/doi.org\/10.1016\/j.tourman.2017.10.014","journal-title":"Tour. Manage."},{"key":"1074_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.tourman.2020.104263","volume":"84","author":"M Hu","year":"2021","unstructured":"Hu, M., Qiu, R.T.R., Wu, D.C., Song, H.: Hierarchical pattern recognition for tourism demand forecasting. Tour. Manage. 84, 104263 (2021). https:\/\/doi.org\/10.1016\/j.tourman.2020.104263","journal-title":"Tour. Manage."},{"issue":"1","key":"1074_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2023.103523","volume":"61","author":"J Gao","year":"2024","unstructured":"Gao, J., Peng, P., Lu, F., Claramunt, C., Qiu, P., Xu, Y.: Mining tourist preferences and decision support via tourism-oriented knowledge graph. Inform. Process. Manag. 61(1), 103523 (2024). https:\/\/doi.org\/10.1016\/j.ipm.2023.103523","journal-title":"Inform. Process. Manag."},{"key":"1074_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.tourman.2022.104490","volume":"90","author":"M Hu","year":"2022","unstructured":"Hu, M., Li, H., Song, H., Li, X., Law, R.: Tourism demand forecasting using tourist-generated online review data. Tour. Manage. 90, 104490 (2022). https:\/\/doi.org\/10.1016\/j.tourman.2022.104490","journal-title":"Tour. Manage."},{"issue":"8","key":"1074_CR8","doi-asserted-by":"publisher","first-page":"2021","DOI":"10.1177\/13548166211025160","volume":"28","author":"S Sun","year":"2022","unstructured":"Sun, S., Li, Y., Guo, J.-E., Wang, S.: Tourism demand forecasting: An ensemble deep learning approach. Tour. Econ. 28(8), 2021\u20132049 (2022). https:\/\/doi.org\/10.1177\/13548166211025160","journal-title":"Tour. Econ."},{"key":"1074_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.tmp.2023.101116","volume":"47","author":"T Hu","year":"2023","unstructured":"Hu, T., Wang, H., Law, R., Geng, J.: Diverse feature extraction techniques in internet search query to forecast tourism demand: An in-depth comparison. Tourism Manag. Perspectives 47, 101116 (2023). https:\/\/doi.org\/10.1016\/j.tmp.2023.101116","journal-title":"Tourism Manag. Perspectives"},{"key":"1074_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2025.112966","volume":"310","author":"C Rojas","year":"2025","unstructured":"Rojas, C., Jatowt, A.: Transformer-based probabilistic forecasting of daily hotel demand using web search behavior. Knowl.-Based Syst. 310, 112966 (2025). https:\/\/doi.org\/10.1016\/j.knosys.2025.112966","journal-title":"Knowl.-Based Syst."},{"issue":"5","key":"1074_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2025.104161","volume":"62","author":"W Liu","year":"2025","unstructured":"Liu, W., Li, H., Zhang, H., Sun, S., Huang, Z., Xie, W.: Tourism demand point-interval forecasting using global-local information extraction network. Inform. Process. Manag. 62(5), 104161 (2025). https:\/\/doi.org\/10.1016\/j.ipm.2025.104161","journal-title":"Inform. Process. Manag."},{"key":"1074_CR12","doi-asserted-by":"publisher","unstructured":"Yang, Y., Fik, T.J., Zhang, H.-l.: Designing a tourism spillover index based on multidestination travel: A two-stage distance-based modeling approach. J. Travel Res. 56(3), 317\u2013333 (2017) https:\/\/doi.org\/10.1177\/0047287516641782","DOI":"10.1177\/0047287516641782"},{"key":"1074_CR13","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1016\/j.annals.2018.12.024","volume":"75","author":"Y Yang","year":"2019","unstructured":"Yang, Y., Zhang, H.: Spatial-temporal forecasting of tourism demand. Ann. Tour. Res. 75, 106\u2013119 (2019). https:\/\/doi.org\/10.1016\/j.annals.2018.12.024","journal-title":"Ann. Tour. Res."},{"key":"1074_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.annals.2022.103384","volume":"94","author":"C Li","year":"2022","unstructured":"Li, C., Zheng, W., Ge, P.: Tourism demand forecasting with spatiotemporal features. Ann. Tour. Res. 94, 103384 (2022). https:\/\/doi.org\/10.1016\/j.annals.2022.103384","journal-title":"Ann. Tour. Res."},{"key":"1074_CR15","doi-asserted-by":"publisher","first-page":"338","DOI":"10.1016\/j.annals.2018.12.001","volume":"75","author":"H Song","year":"2019","unstructured":"Song, H., Qiu, R.T.R., Park, J.: A review of research on tourism demand forecasting: Launching the annals of tourism research curated collection on tourism demand forecasting. Ann. Tour. Res. 75, 338\u2013362 (2019). https:\/\/doi.org\/10.1016\/j.annals.2018.12.001","journal-title":"Ann. Tour. Res."},{"key":"1074_CR16","doi-asserted-by":"publisher","first-page":"660","DOI":"10.1016\/j.apr.2017.01.002","volume":"8","author":"C Zafra","year":"2017","unstructured":"Zafra, C., \u00c1ngel, Y., Torres, E.: Arima analysis of the effect of land surface coverage on pm10 concentrations in a high-altitude megacity. Atmos. Pollut. Res. 8, 660\u2013668 (2017)","journal-title":"Atmos. Pollut. Res."},{"key":"1074_CR17","doi-asserted-by":"publisher","first-page":"398","DOI":"10.1080\/14616688.2016.1253765","volume":"19","author":"A Chhetri","year":"2017","unstructured":"Chhetri, A., Chhetri, P.K., Arrowsmith, C., Corcoran, J.: Modelling tourism and hospitality employment clusters: a spatial econometric approach. Tour. Geogr. 19, 398\u2013424 (2017)","journal-title":"Tour. Geogr."},{"issue":"1","key":"1074_CR18","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1007\/s10586-024-04684-0","volume":"28","author":"M Sakib","year":"2024","unstructured":"Sakib, M., Mustajab, S., Alam, M.: Ensemble deep learning techniques for time series analysis: a comprehensive review, applications, open issues, challenges, and future directions. Clust. Comput. 28(1), 73 (2024). https:\/\/doi.org\/10.1007\/s10586-024-04684-0","journal-title":"Clust. Comput."},{"issue":"3","key":"1074_CR19","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1177\/1354816618812588","volume":"25","author":"EX Jiao","year":"2019","unstructured":"Jiao, E.X., Chen, J.L.: Tourism forecasting: A review of methodological developments over the last decade. Tour. Econ. 25(3), 469\u2013492 (2019). https:\/\/doi.org\/10.1177\/1354816618812588","journal-title":"Tour. Econ."},{"key":"1074_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.tourman.2020.104245","volume":"83","author":"X Li","year":"2021","unstructured":"Li, X., Law, R., Xie, G., Wang, S.: Review of tourism forecasting research with internet data. Tour. Manage. 83, 104245 (2021). https:\/\/doi.org\/10.1016\/j.tourman.2020.104245","journal-title":"Tour. Manage."},{"key":"1074_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.annals.2020.102899","volume":"82","author":"Y Zhang","year":"2020","unstructured":"Zhang, Y., Li, G., Muskat, B., Law, R., Yang, Y.: Group pooling for deep tourism demand forecasting. Ann. Tour. Res. 82, 102899 (2020). https:\/\/doi.org\/10.1016\/j.annals.2020.102899","journal-title":"Ann. Tour. Res."},{"key":"1074_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.annals.2023.103675","volume":"103","author":"S Xu","year":"2023","unstructured":"Xu, S., Liu, Y., Jin, C.: Forecasting daily tourism demand with multiple factors. Ann. Tour. Res. 103, 103675 (2023). https:\/\/doi.org\/10.1016\/j.annals.2023.103675","journal-title":"Ann. Tour. Res."},{"key":"1074_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.121388","volume":"236","author":"X Li","year":"2024","unstructured":"Li, X., Zhang, X., Zhang, C., Wang, S.: Forecasting tourism demand with a novel robust decomposition and ensemble framework. Expert Syst. Appl. 236, 121388 (2024). https:\/\/doi.org\/10.1016\/j.eswa.2023.121388","journal-title":"Expert Syst. Appl."},{"key":"1074_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.annals.2020.102891","volume":"81","author":"G Xie","year":"2020","unstructured":"Xie, G., Qian, Y., Wang, S.: A decomposition-ensemble approach for tourism forecasting. Ann. Tour. Res. 81, 102891 (2020). https:\/\/doi.org\/10.1016\/j.annals.2020.102891","journal-title":"Ann. Tour. Res."},{"key":"1074_CR25","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1016\/j.jhtm.2023.03.021","volume":"55","author":"K-J Lee","year":"2023","unstructured":"Lee, K.-J., Choi, S.-Y.: A comparative wavelet analysis of the differential sensitivities of leisure and business travel to geopolitical risks and economic policy uncertainty: A study of inbound travel to south korea. J. Hosp. Tour. Manag. 55, 202\u2013208 (2023). https:\/\/doi.org\/10.1016\/j.jhtm.2023.03.021","journal-title":"J. Hosp. Tour. Manag."},{"key":"1074_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2024.131071","volume":"295","author":"X Wang","year":"2024","unstructured":"Wang, X., Ma, W.: A hybrid deep learning model with an optimal strategy based on improved vmd and transformer for short-term photovoltaic power forecasting. Energy 295, 131071 (2024). https:\/\/doi.org\/10.1016\/j.energy.2024.131071","journal-title":"Energy"},{"key":"1074_CR27","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.jhtm.2021.08.022","volume":"49","author":"K He","year":"2021","unstructured":"He, K., Ji, L., Wu, C.W.D., Tso, K.F.G.: Using sarima-cnn-lstm approach to forecast daily tourism demand. J. Hosp. Tour. Manag. 49, 25\u201333 (2021). https:\/\/doi.org\/10.1016\/j.jhtm.2021.08.022","journal-title":"J. Hosp. Tour. Manag."},{"key":"1074_CR28","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1016\/j.jhtm.2019.11.003","volume":"42","author":"MA Shehhi","year":"2020","unstructured":"Shehhi, M.A., Karathanasopoulos, A.: Forecasting hotel room prices in selected gcc cities using deep learning. J. Hosp. Tour. Manag. 42, 40\u201350 (2020). https:\/\/doi.org\/10.1016\/j.jhtm.2019.11.003","journal-title":"J. Hosp. Tour. Manag."},{"issue":"1","key":"1074_CR29","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1177\/0047287518824158","volume":"59","author":"X Li","year":"2020","unstructured":"Li, X., Law, R.: Forecasting tourism demand with decomposed search cycles. J. Travel Res. 59(1), 52\u201368 (2020). https:\/\/doi.org\/10.1177\/0047287518824158","journal-title":"J. Travel Res."},{"issue":"1","key":"1074_CR30","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1177\/0047287517737191","volume":"58","author":"JL Chen","year":"2019","unstructured":"Chen, J.L., Li, G., Wu, D.C., Shen, S.: Forecasting seasonal tourism demand using a multiseries structural time series method. J. Travel Res. 58(1), 92\u2013103 (2019). https:\/\/doi.org\/10.1177\/0047287517737191","journal-title":"J. Travel Res."},{"issue":"5","key":"1074_CR31","doi-asserted-by":"publisher","first-page":"832","DOI":"10.1002\/jtr.2445","volume":"23","author":"C Zhang","year":"2021","unstructured":"Zhang, C., Jiang, F., Wang, S., Sun, S.: A new decomposition ensemble approach for tourism demand forecasting: Evidence from major source countries in asia-pacific region. Int. J. Tour. Res. 23(5), 832\u2013845 (2021). https:\/\/doi.org\/10.1002\/jtr.2445","journal-title":"Int. J. Tour. Res."},{"key":"1074_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.122930","volume":"244","author":"Z Liao","year":"2024","unstructured":"Liao, Z., Ren, C., Sun, F., Tao, Y., Li, W.: Emd-based model with cooperative training mechanism for tourism demand forecasting. Expert Syst. Appl. 244, 122930 (2024). https:\/\/doi.org\/10.1016\/j.eswa.2023.122930","journal-title":"Expert Syst. Appl."},{"issue":"3","key":"1074_CR33","doi-asserted-by":"publisher","first-page":"330","DOI":"10.1177\/1354816618768318","volume":"25","author":"A Saayman","year":"2019","unstructured":"Saayman, A., Klerk, J.: Forecasting tourist arrivals using multivariate singular spectrum analysis. Tour. Econ. 25(3), 330\u2013354 (2019). https:\/\/doi.org\/10.1177\/1354816618768318","journal-title":"Tour. Econ."},{"issue":"7","key":"1074_CR34","doi-asserted-by":"publisher","first-page":"1682","DOI":"10.1177\/00472875211036194","volume":"61","author":"C Zhang","year":"2022","unstructured":"Zhang, C., Li, M., Sun, S., Tang, L., Wang, S.: Decomposition methods for tourism demand forecasting: A comparative study. J. Travel Res. 61(7), 1682\u20131699 (2022). https:\/\/doi.org\/10.1177\/00472875211036194","journal-title":"J. Travel Res."},{"key":"1074_CR35","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1016\/j.annals.2017.01.008","volume":"63","author":"H Hassani","year":"2017","unstructured":"Hassani, H., Silva, E.S., Antonakakis, N., Filis, G., Gupta, R.: Forecasting accuracy evaluation of tourist arrivals. Ann. Tour. Res. 63, 112\u2013127 (2017). https:\/\/doi.org\/10.1016\/j.annals.2017.01.008","journal-title":"Ann. Tour. Res."},{"issue":"5","key":"1074_CR36","doi-asserted-by":"publisher","first-page":"981","DOI":"10.1177\/0047287520919522","volume":"60","author":"Y Zhang","year":"2021","unstructured":"Zhang, Y., Li, G., Muskat, B., Law, R.: Tourism demand forecasting: A decomposed deep learning approach. J. Travel Res. 60(5), 981\u2013997 (2021). https:\/\/doi.org\/10.1177\/0047287520919522","journal-title":"J. Travel Res."},{"key":"1074_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.annals.2021.103271","volume":"90","author":"W Zheng","year":"2021","unstructured":"Zheng, W., Huang, L., Lin, Z.: Multi-attraction, hourly tourism demand forecasting. Ann. Tour. Res. 90, 103271 (2021). https:\/\/doi.org\/10.1016\/j.annals.2021.103271","journal-title":"Ann. Tour. Res."},{"key":"1074_CR38","doi-asserted-by":"publisher","unstructured":"Huang, G., Li, X., Zhang, B., Ren, J.: Pm2.5 concentration forecasting at surface monitoring sites using gru neural network based on empirical mode decomposition. Science of The Total Environment 768, 144516 (2021) https:\/\/doi.org\/10.1016\/j.scitotenv.2020.144516","DOI":"10.1016\/j.scitotenv.2020.144516"},{"key":"1074_CR39","unstructured":"Oreshkin, B.N., Carpov, D., Chapados, N., Bengio, Y.: N-BEATS: Neural basis expansion analysis for interpretable time series forecasting. In: International Conference on Learning Representations (2020)"},{"issue":"6","key":"1074_CR40","doi-asserted-by":"publisher","first-page":"6989","DOI":"10.1609\/aaai.v37i6.25854","volume":"37","author":"C Challu","year":"2023","unstructured":"Challu, C., Olivares, K.G., Oreshkin, B.N., Garza Ramirez, F., Mergenthaler Canseco, M., Dubrawski, A.: NHITS: Neural hierarchical interpolation for time series forecasting. Proceed. AAAI Conf. Artificial Intell. 37(6), 6989\u20136997 (2023). https:\/\/doi.org\/10.1609\/aaai.v37i6.25854","journal-title":"Proceed. AAAI Conf. Artificial Intell."},{"issue":"1","key":"1074_CR41","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1007\/s10586-024-04684-0","volume":"28","author":"M Sakib","year":"2024","unstructured":"Sakib, M., Mustajab, S., Alam, M.: Ensemble deep learning techniques for time series analysis: a comprehensive review, applications, open issues, challenges, and future directions. Clust. Comput. 28(1), 73 (2024). https:\/\/doi.org\/10.1007\/s10586-024-04684-0","journal-title":"Clust. Comput."},{"key":"1074_CR42","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2022.124967","volume":"259","author":"C Li","year":"2022","unstructured":"Li, C., Li, G., Wang, K., Han, B.: A multi-energy load forecasting method based on parallel architecture cnn-gru and transfer learning for data deficient integrated energy systems. Energy 259, 124967 (2022). https:\/\/doi.org\/10.1016\/j.energy.2022.124967","journal-title":"Energy"},{"key":"1074_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2021.121082","volume":"233","author":"J Wang","year":"2021","unstructured":"Wang, J., Cao, J., Yuan, S., Cheng, M.: Short-term forecasting of natural gas prices by using a novel hybrid method based on a combination of the ceemdan-se-and the pso-als-optimized gru network. Energy 233, 121082 (2021). https:\/\/doi.org\/10.1016\/j.energy.2021.121082","journal-title":"Energy"},{"key":"1074_CR44","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.124954","volume":"257","author":"X Li","year":"2024","unstructured":"Li, X., Zhang, Y., Chen, L., Li, J., Chu, X.: A decomposition-ensemble-integration framework for carbon price forecasting. Expert Syst. Appl. 257, 124954 (2024). https:\/\/doi.org\/10.1016\/j.eswa.2024.124954","journal-title":"Expert Syst. Appl."},{"key":"1074_CR45","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2024.124798","volume":"378","author":"Z Lin","year":"2025","unstructured":"Lin, Z., Lin, T., Li, J., Li, C.: A novel short-term multi-energy load forecasting method for integrated energy system based on two-layer joint modal decomposition and dynamic optimal ensemble learning. Appl. Energy 378, 124798 (2025). https:\/\/doi.org\/10.1016\/j.apenergy.2024.124798","journal-title":"Appl. Energy"},{"key":"1074_CR46","doi-asserted-by":"publisher","first-page":"688","DOI":"10.1016\/j.renene.2021.04.091","volume":"174","author":"S Zhang","year":"2021","unstructured":"Zhang, S., Chen, Y., Xiao, J., Zhang, W., Feng, R.: Hybrid wind speed forecasting model based on multivariate data secondary decomposition approach and deep learning algorithm with attention mechanism. Renewable Energy 174, 688\u2013704 (2021). https:\/\/doi.org\/10.1016\/j.renene.2021.04.091","journal-title":"Renewable Energy"},{"key":"1074_CR47","doi-asserted-by":"publisher","unstructured":"Moradian daghigh, A., Mirzaee Ghazani, M.: Analyzing the drivers of co2 allowance prices in eu ets under the covid-19 pandemic: Considering memd approach with a novel filtering procedure. Journal of Cleaner Production 427, 139043 (2023) https:\/\/doi.org\/10.1016\/j.jclepro.2023.139043","DOI":"10.1016\/j.jclepro.2023.139043"},{"key":"1074_CR48","doi-asserted-by":"publisher","unstructured":"Rehman, N.u., Aftab, H.: Multivariate variational mode decomposition. IEEE Trans. Signal Process. 67(23), 6039\u20136052 (2019). https:\/\/doi.org\/10.1109\/TSP.2019.2951223","DOI":"10.1109\/TSP.2019.2951223"},{"issue":"2117","key":"1074_CR49","doi-asserted-by":"publisher","first-page":"1291","DOI":"10.1098\/rspa.2009.0502","volume":"466","author":"N Rehman","year":"2009","unstructured":"Rehman, N., Mandic, D.P.: Multivariate empirical mode decomposition. Proceed. Royal Soc. A 466(2117), 1291\u20131302 (2009). https:\/\/doi.org\/10.1098\/rspa.2009.0502","journal-title":"Proceed. Royal Soc. A"},{"key":"1074_CR50","doi-asserted-by":"publisher","unstructured":"Heidari, A.A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., Chen, H.: Harris hawks optimization: Algorithm and applications. Futur. Gener. Comput. Syst. 97, 849\u2013872 (2019). https:\/\/doi.org\/10.1016\/j.future.2019.02.028","DOI":"10.1016\/j.future.2019.02.028"},{"key":"1074_CR51","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2024.112222","volume":"166","author":"G Xie","year":"2024","unstructured":"Xie, G., Liu, S., Dong, H., Huang, X.: Interval forecasting of baltic dry index within a secondary decomposition-ensemble methodology. Appl. Soft Comput. 166, 112222 (2024). https:\/\/doi.org\/10.1016\/j.asoc.2024.112222","journal-title":"Appl. Soft Comput."},{"key":"1074_CR52","doi-asserted-by":"publisher","DOI":"10.1016\/j.jsv.2019.115099","volume":"468","author":"Y Zhao","year":"2020","unstructured":"Zhao, Y., Li, C., Fu, W., Liu, J., Yu, T., Chen, H.: A modified variational mode decomposition method based on envelope nesting and multi-criteria evaluation. J. Sound Vib. 468, 115099 (2020). https:\/\/doi.org\/10.1016\/j.jsv.2019.115099","journal-title":"J. Sound Vib."},{"issue":"2","key":"1074_CR53","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1109\/TNSRE.2007.897025","volume":"15","author":"W Chen","year":"2007","unstructured":"Chen, W., Wang, Z., Xie, H., Yu, W.: Characterization of surface emg signal based on fuzzy entropy. IEEE Trans. Neural Syst. Rehabil. Eng. 15(2), 266\u2013272 (2007). https:\/\/doi.org\/10.1109\/TNSRE.2007.897025","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"1074_CR54","unstructured":"Chung, J., G\u00fcl\u00e7ehre, \u00c7., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. CoRR abs\/1412.3555 (2014) arXiv:1412.3555"},{"issue":"3","key":"1074_CR55","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1080\/07350015.1995.10524599","volume":"13","author":"FX Diebold","year":"1995","unstructured":"Diebold, F.X., Mariano, R.S.: Comparing predictive accuracy. J. Business & Econ. Statistics 13(3), 253\u2013263 (1995). https:\/\/doi.org\/10.1080\/07350015.1995.10524599","journal-title":"J. Business & Econ. Statistics"},{"key":"1074_CR56","doi-asserted-by":"publisher","DOI":"10.1016\/j.eneco.2024.107952","volume":"139","author":"K Yang","year":"2024","unstructured":"Yang, K., Sun, Y., Hong, Y., Wang, S.: Forecasting interval carbon price through a multi-scale interval-valued decomposition ensemble approach. Energy Economics 139, 107952 (2024). https:\/\/doi.org\/10.1016\/j.eneco.2024.107952","journal-title":"Energy Economics"}],"container-title":["International Journal of Data Science and Analytics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41060-026-01074-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s41060-026-01074-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41060-026-01074-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T10:59:33Z","timestamp":1773140373000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s41060-026-01074-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,10]]},"references-count":56,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,12]]}},"alternative-id":["1074"],"URL":"https:\/\/doi.org\/10.1007\/s41060-026-01074-0","relation":{},"ISSN":["2364-415X","2364-4168"],"issn-type":[{"value":"2364-415X","type":"print"},{"value":"2364-4168","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,10]]},"assertion":[{"value":"10 June 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 February 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 March 2026","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 no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"93"}}