乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,27]],"date-time":"2024-09-27T04:20:25Z","timestamp":1727410825701},"reference-count":68,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2022,11,1]],"date-time":"2022-11-01T00:00:00Z","timestamp":1667260800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2022,11,1]],"date-time":"2022-11-01T00:00:00Z","timestamp":1667260800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2022,11,1]],"date-time":"2022-11-01T00:00:00Z","timestamp":1667260800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2022,11,1]],"date-time":"2022-11-01T00:00:00Z","timestamp":1667260800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2022,11,1]],"date-time":"2022-11-01T00:00:00Z","timestamp":1667260800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,11,1]],"date-time":"2022-11-01T00:00:00Z","timestamp":1667260800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems with Applications"],"published-print":{"date-parts":[[2022,11]]},"DOI":"10.1016\/j.eswa.2022.117707","type":"journal-article","created":{"date-parts":[[2022,6,2]],"date-time":"2022-06-02T01:54:43Z","timestamp":1654134883000},"page":"117707","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":9,"special_numbering":"C","title":["Linear and nonlinear framework for interval-valued PM2.5 concentration forecasting based on multi-factor interval division strategy and bivariate empirical mode decomposition"],"prefix":"10.1016","volume":"205","author":[{"given":"Zicheng","family":"Wang","sequence":"first","affiliation":[]},{"given":"Hao","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-7312-0367","authenticated-orcid":false,"given":"Huayou","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Zhenni","family":"Ding","sequence":"additional","affiliation":[]},{"given":"Jiaming","family":"Zhu","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"3","key":"10.1016\/j.eswa.2022.117707_b0005","doi-asserted-by":"crossref","first-page":"285","DOI":"10.22190\/FUME190327035A","article-title":"Results and challenges of artificial neural networks used for decision-making and control in medical applications","volume":"17","author":"Albu","year":"2019","journal-title":"Facta Universitatis, Series: Mechanical Engineering"},{"issue":"2","key":"10.1016\/j.eswa.2022.117707_b0010","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1007\/s10614-010-9230-2","article-title":"Different approaches to forecast interval time series: A comparison in finance","volume":"37","author":"Arroyo","year":"2011","journal-title":"Computational Economics"},{"key":"10.1016\/j.eswa.2022.117707_b0015","series-title":"Proceedings of the 1st European Symposium on Time Series Prediction","first-page":"231","article-title":"Exponential smoothing methods for interval time series","author":"Arroyo","year":"2007"},{"issue":"4","key":"10.1016\/j.eswa.2022.117707_b0020","first-page":"652","article-title":"Recursive neural network model for analysis and forecast of PM10 and PM2.5. Atmospheric","volume":"8","author":"Biancofiore","year":"2017","journal-title":"Pollution Research"},{"year":"2000","author":"Bock","series-title":"Analysis of symbolic data: Exploratory methods for extracting statistical information from complex data","key":"10.1016\/j.eswa.2022.117707_b0025"},{"doi-asserted-by":"crossref","unstructured":"Braggio, J. T., Hall, E. S., Weber, S. A., & Huff, A. K. (2021). Contribution of AOD-PM2.5 surfaces to respiratory-cardiovascular hospital events in urban and rural areas in Baltimore, Maryland, USA: New analytical method correctly identified true positive cases and true negative controls. Atmospheric Environment, 262, 118629.","key":"10.1016\/j.eswa.2022.117707_b0030","DOI":"10.1016\/j.atmosenv.2021.118629"},{"issue":"3","key":"10.1016\/j.eswa.2022.117707_b0035","doi-asserted-by":"crossref","first-page":"800","DOI":"10.1016\/j.ijforecast.2019.09.003","article-title":"An information-theoretic approach for forecasting interval-valued SP500 daily returns","volume":"36","author":"Buansing","year":"2020","journal-title":"International Journal of Forecasting"},{"key":"10.1016\/j.eswa.2022.117707_b0040","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/j.envsoft.2019.06.014","article-title":"A review of artificial neural network models for ambient air pollution prediction","volume":"119","author":"Cabaneros","year":"2019","journal-title":"Environmental Modelling & Software"},{"issue":"8","key":"10.1016\/j.eswa.2022.117707_b0045","doi-asserted-by":"crossref","first-page":"10368","DOI":"10.1016\/j.eswa.2011.02.049","article-title":"Combining linear and nonlinear model in forecasting tourism demand","volume":"38","author":"Chen","year":"2011","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2022.117707_b0050","doi-asserted-by":"crossref","DOI":"10.1016\/j.atmosenv.2020.117909","article-title":"Evaluation of real-time PM2.5 forecasts with the WRF-CMAQ modeling system and weather-pattern-dependent bias-adjusted PM2.5 forecasts in Taiwan","volume":"244","author":"Cheng","year":"2021","journal-title":"Atmospheric Environment"},{"key":"10.1016\/j.eswa.2022.117707_b0055","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2019.105970","article-title":"A hybrid optimized error correction system for time series forecasting","volume":"87","author":"de Oliveira","year":"2020","journal-title":"Applied Soft Computing"},{"key":"10.1016\/j.eswa.2022.117707_b0060","doi-asserted-by":"crossref","DOI":"10.1016\/j.enconman.2020.112779","article-title":"A hybrid model based on data preprocessing strategy and error correction system for wind speed forecasting","volume":"212","author":"Deng","year":"2020","journal-title":"Energy Conversion and Management"},{"key":"10.1016\/j.eswa.2022.117707_b0065","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2020.106620","article-title":"A novel hybrid model based on multi-objective Harris hawks optimization algorithm for daily PM2.5 and PM10 forecasting","volume":"96","author":"Du","year":"2020","journal-title":"Applied Soft Computing"},{"issue":"2","key":"10.1016\/j.eswa.2022.117707_b0070","doi-asserted-by":"crossref","first-page":"715","DOI":"10.1016\/j.enpol.2009.10.007","article-title":"Electric power demand forecasting using interval time series: A comparison between VAR and iMLP","volume":"38","author":"Garc\u00eda-Ascanio","year":"2010","journal-title":"Energy Policy"},{"key":"10.1016\/j.eswa.2022.117707_b0075","series-title":"A bibliometric and visualized analysis of research progress and frontiers on health effects caused by PM 2.5","first-page":"1","author":"Han","year":"2021"},{"issue":"6","key":"10.1016\/j.eswa.2022.117707_b0080","doi-asserted-by":"crossref","first-page":"1499","DOI":"10.1016\/j.eneco.2010.07.012","article-title":"An empirical model of daily highs and lows of West Texas Intermediate crude oil prices","volume":"32","author":"He","year":"2010","journal-title":"Energy Economics"},{"issue":"11","key":"10.1016\/j.eswa.2022.117707_b0085","doi-asserted-by":"crossref","first-page":"1191","DOI":"10.1080\/02664760802578304","article-title":"Predicting daily highs and lows of exchange rates: A cointegration analysis","volume":"36","author":"He","year":"2009","journal-title":"Journal of Applied Statistics"},{"issue":"3","key":"10.1016\/j.eswa.2022.117707_b0090","doi-asserted-by":"crossref","first-page":"1313","DOI":"10.1002\/asjc.2494","article-title":"Tensor product-based model transformation approach to tower crane systems modeling","volume":"23","author":"Hedrea","year":"2021","journal-title":"Asian Journal of Control"},{"issue":"1","key":"10.1016\/j.eswa.2022.117707_b0095","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1080\/00401706.1970.10488634","article-title":"Ridge regression: Biased estimation for nonorthogonal problems","volume":"12","author":"Hoerl","year":"1970","journal-title":"Technometrics"},{"key":"10.1016\/j.eswa.2022.117707_b0100","doi-asserted-by":"crossref","DOI":"10.1016\/j.enconman.2021.114162","article-title":"Two novel hybrid linear and nonlinear models for wind speed forecasting","volume":"238","author":"Huang","year":"2021","journal-title":"Energy Conversion and Management"},{"key":"10.1016\/j.eswa.2022.117707_b0105","article-title":"Association between ambient air pollution and childhood respiratory diseases in low-and middle-income Asian countries: A systematic review","volume":"118422","author":"Ibrahim","year":"2021","journal-title":"Atmospheric Environment"},{"issue":"3","key":"10.1016\/j.eswa.2022.117707_b0110","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/LES.2010.2051413","article-title":"Accurate machine-learning-based on-chip router modeling","volume":"2","author":"Jeong","year":"2010","journal-title":"IEEE Embedded Systems Letters"},{"key":"10.1016\/j.eswa.2022.117707_b0115","doi-asserted-by":"crossref","first-page":"468","DOI":"10.1016\/j.energy.2019.02.080","article-title":"Modeling for chaotic time series based on linear and nonlinear framework: Application to wind speed forecasting","volume":"173","author":"Jiang","year":"2019","journal-title":"Energy"},{"key":"10.1016\/j.eswa.2022.117707_b0120","series-title":"IEEE International Conference on Neural Networks","first-page":"1942","article-title":"Particle Swarm Optimization (PSO), Proc","author":"Kennedy","year":"1995"},{"issue":"8","key":"10.1016\/j.eswa.2022.117707_b0125","doi-asserted-by":"crossref","first-page":"6994","DOI":"10.1016\/j.eswa.2012.01.048","article-title":"An experience-based system supporting inventory planning: A fuzzy approach","volume":"39","author":"Ketsarapong","year":"2012","journal-title":"Expert Systems with Applications"},{"issue":"6","key":"10.1016\/j.eswa.2022.117707_b0135","doi-asserted-by":"crossref","first-page":"686","DOI":"10.3390\/atmos12060686","article-title":"Statistical Approaches for Forecasting Primary Air Pollutants: A Review","volume":"12","author":"Liao","year":"2021","journal-title":"Atmosphere"},{"key":"10.1016\/j.eswa.2022.117707_b0140","first-page":"1","article-title":"Deep Learning for Air Quality Forecasts: A Review","author":"Liao","year":"2020","journal-title":"Current Pollution Reports"},{"issue":"1","key":"10.1016\/j.eswa.2022.117707_b0145","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/S1364-8152(01)00050-0","article-title":"Effect of PM2.5 on AQI in Taiwan","volume":"17","author":"Liu","year":"2002","journal-title":"Environmental Modelling & Software"},{"key":"10.1016\/j.eswa.2022.117707_b0150","article-title":"Intelligent modeling strategies for forecasting air quality time series: A review","volume":"106957","author":"Liu","year":"2021","journal-title":"Applied Soft Computing"},{"key":"10.1016\/j.eswa.2022.117707_b0155","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.atmosres.2017.10.009","article-title":"Research and application of a novel hybrid decomposition-ensemble learning paradigm with error correction for daily PM10 forecasting","volume":"201","author":"Luo","year":"2018","journal-title":"Atmospheric Research"},{"key":"10.1016\/j.eswa.2022.117707_b0160","doi-asserted-by":"crossref","DOI":"10.1016\/j.enconman.2019.112345","article-title":"Application of hybrid model based on double decomposition, error correction and deep learning in short-term wind speed prediction","volume":"205","author":"Ma","year":"2020","journal-title":"Energy Conversion and Management"},{"issue":"2","key":"10.1016\/j.eswa.2022.117707_b0165","doi-asserted-by":"crossref","DOI":"10.1007\/s10614-020-09978-0","article-title":"Functional Fuzzy Rule-Based Modeling for Interval-Valued Data: An Empirical Application for Exchange Rates Forecasting","volume":"57","author":"Maciel","year":"2021","journal-title":"Computational Economics"},{"issue":"3","key":"10.1016\/j.eswa.2022.117707_b0170","doi-asserted-by":"crossref","first-page":"740","DOI":"10.1016\/j.ijforecast.2010.02.012","article-title":"Holt\u2019s exponential smoothing and neural network models for forecasting interval-valued time series","volume":"27","author":"Maia","year":"2011","journal-title":"International Journal of Forecasting"},{"issue":"16\u201318","key":"10.1016\/j.eswa.2022.117707_b0175","doi-asserted-by":"crossref","first-page":"3344","DOI":"10.1016\/j.neucom.2008.02.022","article-title":"Forecasting models for interval-valued time series","volume":"71","author":"Maia","year":"2008","journal-title":"Neurocomputing"},{"key":"10.1016\/j.eswa.2022.117707_b0180","doi-asserted-by":"crossref","DOI":"10.1016\/j.envint.2021.106763","article-title":"Estimating the health and economic burden of shipping related air pollution in the Iberian Peninsula","volume":"156","author":"Nunes","year":"2021","journal-title":"Environment International"},{"key":"10.1016\/j.eswa.2022.117707_b0185","series-title":"August). Multi-dimensional function approximation and regression estimation","first-page":"757","author":"P\u00e9rez-Cruz","year":"2002"},{"issue":"2","key":"10.1016\/j.eswa.2022.117707_b0190","first-page":"21","article-title":"Applications of signatures to expert systems modelling","volume":"11","author":"Pozna","year":"2014","journal-title":"Acta Polytechnica Hungarica"},{"issue":"7","key":"10.1016\/j.eswa.2022.117707_b0195","doi-asserted-by":"crossref","first-page":"4625","DOI":"10.1109\/TIM.2020.2983531","article-title":"Evolving fuzzy models for prosthetic hand myoelectric-based control","volume":"69","author":"Precup","year":"2020","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"issue":"14","key":"10.1016\/j.eswa.2022.117707_b0200","doi-asserted-by":"crossref","first-page":"936","DOI":"10.1109\/LSP.2007.904710","article-title":"Bivariate Empirical Mode Decomposition","volume":"12","author":"Rilling","year":"2007","journal-title":"IEEE Signal Processing Letters"},{"key":"10.1016\/j.eswa.2022.117707_b0205","article-title":"Health impact assessment of air pollution in the metropolitan region of Fortaleza, Cear\u00e1","volume":"241","author":"Rocha","year":"2020","journal-title":"Brazil. Atmospheric Environment"},{"issue":"10","key":"10.1016\/j.eswa.2022.117707_b0210","doi-asserted-by":"crossref","first-page":"1534","DOI":"10.1016\/j.simpat.2007.11.021","article-title":"European operational air quality forecasting system by using MM5\u2013CMAQ\u2013EMIMO tool","volume":"16","author":"San Jos\u00e9","year":"2008","journal-title":"Simulation Modelling Practice and Theory"},{"issue":"2","key":"10.1016\/j.eswa.2022.117707_b0215","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1007\/s11063-007-9035-z","article-title":"iMLP: Applying multi-layer perceptrons to interval-valued data","volume":"25","author":"San Roque","year":"2007","journal-title":"Neural Processing Letters"},{"key":"10.1016\/j.eswa.2022.117707_b0220","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.jes.2020.08.031","article-title":"Insights into measurements of water-soluble ions in PM2. 5 and their gaseous precursors in Beijing","volume":"102","author":"Su","year":"2021","journal-title":"Journal of Environmental Sciences"},{"key":"10.1016\/j.eswa.2022.117707_b0225","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1016\/j.eneco.2018.10.015","article-title":"Interval decomposition ensemble approach for crude oil price forecasting","volume":"76","author":"Sun","year":"2018","journal-title":"Energy Economics"},{"unstructured":"Sun, S., Wang, S., & Wei, Y. (2019). Interval forecasting of exchange rates: a new interval decomposition ensemble approach.","key":"10.1016\/j.eswa.2022.117707_b0230"},{"unstructured":"Thayananthan, A. (2006). Template-based pose estimation and tracking of 3D hand motion (Doctoral dissertation, University of Cambridge).","key":"10.1016\/j.eswa.2022.117707_b0235"},{"issue":"9","key":"10.1016\/j.eswa.2022.117707_b0240","doi-asserted-by":"crossref","first-page":"1302","DOI":"10.1016\/j.patrec.2008.02.004","article-title":"Pose estimation and tracking using multivariate regression","volume":"29","author":"Thayananthan","year":"2008","journal-title":"Pattern Recognition Letters"},{"doi-asserted-by":"crossref","unstructured":"Tian, Y., Zhang, Y., Liang, Y., Niu, Z., Xue, Q., & Feng, Y. (2020). PM2. 5 source apportionment during severe haze episodes in a Chinese megacity based on a 5-month period by using hourly species measurements: Explore how to better conduct PMF during haze episodes. Atmospheric Environment, 224, 117364.","key":"10.1016\/j.eswa.2022.117707_b0245","DOI":"10.1016\/j.atmosenv.2020.117364"},{"issue":"1","key":"10.1016\/j.eswa.2022.117707_b0250","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression shrinkage and selection via the lasso","volume":"58","author":"Tibshirani","year":"1996","journal-title":"Journal of the Royal Statistical Society: Series B (Methodological)"},{"issue":"Jun","key":"10.1016\/j.eswa.2022.117707_b0255","first-page":"211","article-title":"Sparse Bayesian learning and the relevance vector machine","volume":"1","author":"Tipping","year":"2001","journal-title":"Journal of machine learning research"},{"unstructured":"Tipping, M. E., & Faul, A. C. (2003, January). Fast marginal likelihood maximisation for sparse Bayesian models. In International workshop on artificial intelligence and statistics (pp. 276-283). PMLR.","key":"10.1016\/j.eswa.2022.117707_b0260"},{"unstructured":"Wang, S. Y. (2004, April). TEI@ I: a new methodology for studying complex systems. In The International Workshop on Complexity Science, Tsukuba, Japan (Vol. 4).","key":"10.1016\/j.eswa.2022.117707_b0265"},{"key":"10.1016\/j.eswa.2022.117707_b0270","doi-asserted-by":"crossref","DOI":"10.1016\/j.atmosenv.2019.117200","article-title":"An enhanced interval PM2.5 concentration forecasting model based on BEMD and MLPI with influencing factors","volume":"223","author":"Wang","year":"2020","journal-title":"Atmospheric Environment"},{"issue":"30","key":"10.1016\/j.eswa.2022.117707_b0275","doi-asserted-by":"crossref","first-page":"37802","DOI":"10.1007\/s11356-020-09891-x","article-title":"Double decomposition and optimal combination ensemble learning approach for interval-valued AQI forecasting using streaming data","volume":"27","author":"Wang","year":"2020","journal-title":"Environmental Science and Pollution Research"},{"issue":"3","key":"10.1016\/j.eswa.2022.117707_b0280","doi-asserted-by":"crossref","first-page":"5483","DOI":"10.3233\/JIFS-202481","article-title":"Multi-scale deep learning and optimal combination ensemble approach for AQI forecasting using big data with meteorological conditions","volume":"40","author":"Wang","year":"2021","journal-title":"Journal of Intelligent & Fuzzy Systems"},{"key":"10.1016\/j.eswa.2022.117707_b0285","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2021.108110","article-title":"Daily PM2.5 and PM10 forecasting using linear and nonlinear modeling framework based on robust local mean decomposition and moving window ensemble strategy","volume":"114","author":"Wang","year":"2022","journal-title":"Applied Soft Computing"},{"issue":"3","key":"10.1016\/j.eswa.2022.117707_b0290","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1007\/s10874-006-9038-6","article-title":"Modeling of regional high ozone episode observed at two mountain sites (Mt. Tai and Huang) in East China","volume":"55","author":"Wang","year":"2006","journal-title":"Journal of Atmospheric chemistry"},{"key":"10.1016\/j.eswa.2022.117707_b0295","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.knosys.2013.10.012","article-title":"Multiple-output support vector regression with a firefly algorithm for interval-valued stock price index forecasting","volume":"55","author":"Xiong","year":"2014","journal-title":"Knowledge-Based Systems"},{"key":"10.1016\/j.eswa.2022.117707_b0300","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1016\/j.ijepes.2014.06.010","article-title":"Interval forecasting of electricity demand: A novel bivariate EMD-based support vector regression modeling framework","volume":"63","author":"Xiong","year":"2014","journal-title":"International Journal of Electrical Power & Energy Systems"},{"key":"10.1016\/j.eswa.2022.117707_b0305","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.ins.2015.01.029","article-title":"Forecasting interval time series using a fully complex-valued RBF neural network with DPSO and PSO algorithms","volume":"305","author":"Xiong","year":"2015","journal-title":"Information Sciences"},{"key":"10.1016\/j.eswa.2022.117707_b0310","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.knosys.2015.01.002","article-title":"A combination method for interval forecasting of agricultural commodity futures prices","volume":"77","author":"Xiong","year":"2015","journal-title":"Knowledge-Based Systems"},{"key":"10.1016\/j.eswa.2022.117707_b0315","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.econmod.2016.08.019","article-title":"Interval-valued time series forecasting using a novel hybrid HoltI and MSVR model","volume":"60","author":"Xiong","year":"2017","journal-title":"Economic Modelling"},{"key":"10.1016\/j.eswa.2022.117707_b0320","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2019.105972","article-title":"A novel combined forecasting system for air pollutants concentration based on fuzzy theory and optimization of aggregation weight","volume":"87","author":"Yang","year":"2020","journal-title":"Applied Soft Computing"},{"key":"10.1016\/j.eswa.2022.117707_b0325","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/S0925-2312(01)00702-0","article-title":"Time series forecasting using a hybrid ARIMA and neural network model","volume":"50","author":"Zhang","year":"2003","journal-title":"Neurocomputing"},{"key":"10.1016\/j.eswa.2022.117707_b0330","doi-asserted-by":"crossref","first-page":"702","DOI":"10.1016\/j.ecolind.2018.08.032","article-title":"Trend analysis and forecast of PM2. 5 in Fuzhou, China using the ARIMA model","volume":"95","author":"Zhang","year":"2018","journal-title":"Ecological indicators"},{"key":"10.1016\/j.eswa.2022.117707_b0335","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.eneco.2017.12.030","article-title":"A novel multiscale nonlinear ensemble leaning paradigm for carbon price forecasting","volume":"70","author":"Zhu","year":"2018","journal-title":"Energy Economics"},{"key":"10.1016\/j.eswa.2022.117707_b0345","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.atmosenv.2018.04.004","article-title":"PM 2.5 forecasting using SVR with PSOGSA algorithm based on CEEMD, GRNN and GCA considering meteorological factors","volume":"183","author":"Zhu","year":"2018","journal-title":"Atmospheric Environment"},{"issue":"2","key":"10.1016\/j.eswa.2022.117707_b0350","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1111\/j.1467-9868.2005.00503.x","article-title":"Regularization and variable selection via the elastic net","volume":"67","author":"Zou","year":"2005","journal-title":"Journal of the Royal Statistical Society: Series B (Statistical Methodology)"}],"container-title":["Expert Systems with Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417422009952?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417422009952?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,9,26]],"date-time":"2024-09-26T08:17:07Z","timestamp":1727338627000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0957417422009952"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11]]},"references-count":68,"alternative-id":["S0957417422009952"],"URL":"https:\/\/doi.org\/10.1016\/j.eswa.2022.117707","relation":{},"ISSN":["0957-4174"],"issn-type":[{"type":"print","value":"0957-4174"}],"subject":[],"published":{"date-parts":[[2022,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Linear and nonlinear framework for interval-valued PM2.5 concentration forecasting based on multi-factor interval division strategy and bivariate empirical mode decomposition","name":"articletitle","label":"Article Title"},{"value":"Expert Systems with Applications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.eswa.2022.117707","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2022 Elsevier Ltd. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"117707"}}