{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T17:24:15Z","timestamp":1776792255482,"version":"3.51.2"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"29","license":[{"start":{"date-parts":[[2024,12,6]],"date-time":"2024-12-06T00:00:00Z","timestamp":1733443200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,6]],"date-time":"2024-12-06T00:00:00Z","timestamp":1733443200000},"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":["Neural Comput & Applic"],"published-print":{"date-parts":[[2025,10]]},"DOI":"10.1007\/s00521-024-10601-8","type":"journal-article","created":{"date-parts":[[2024,12,6]],"date-time":"2024-12-06T00:39:20Z","timestamp":1733445560000},"page":"23773-23787","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Comparing machine learning algorithms for imputation of missing time series in meteorological data"],"prefix":"10.1007","volume":"37","author":[{"given":"Mohamed","family":"Boujoudar","sequence":"first","affiliation":[]},{"given":"Massaab","family":"El Ydrissi","sequence":"additional","affiliation":[]},{"given":"Mounir","family":"Abraim","sequence":"additional","affiliation":[]},{"given":"Ibtissam","family":"Bouarfa","sequence":"additional","affiliation":[]},{"given":"Omaima","family":"El Alani","sequence":"additional","affiliation":[]},{"given":"Hicham","family":"Ghennioui","sequence":"additional","affiliation":[]},{"given":"El Ghali","family":"Bennouna","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,12,6]]},"reference":[{"key":"10601_CR1","doi-asserted-by":"publisher","first-page":"14643","DOI":"10.1038\/s41598-022-18516-x","volume":"12","author":"E Sarmas","year":"2022","unstructured":"Sarmas E, Dimitropoulos N, Marinakis V et al (2022) Transfer learning strategies for solar power forecasting under data scarcity. Sci Rep 12:14643. https:\/\/doi.org\/10.1038\/s41598-022-18516-x","journal-title":"Sci Rep"},{"key":"10601_CR2","doi-asserted-by":"publisher","DOI":"10.1063\/5.0024750","volume":"12","author":"FS Mohd Chachuli","year":"2020","unstructured":"Mohd Chachuli FS, Ahmad Ludin N, Mat S, Sopian K (2020) Renewable energy performance evaluation studies using the data envelopment analysis (DEA): a systematic review. J Renew Sustain Energy 12:062701. https:\/\/doi.org\/10.1063\/5.0024750","journal-title":"J Renew Sustain Energy"},{"key":"10601_CR3","doi-asserted-by":"crossref","unstructured":"Mack C, Su Z, Weistreich D (2018) Managing missing data in patient registries. Agency for Healthcare Research and Quality (AHRQ)","DOI":"10.23970\/AHRQREGISTRIESMISSINGDATA"},{"key":"10601_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.chemolab.2022.104518","volume":"223","author":"HV Bhagat","year":"2022","unstructured":"Bhagat HV, Singh M (2022) NMVI: a data-splitting based imputation technique for distinct types of missing data. Chemom Intell Lab Syst 223:104518. https:\/\/doi.org\/10.1016\/j.chemolab.2022.104518","journal-title":"Chemom Intell Lab Syst"},{"key":"10601_CR5","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1504\/IJBDM.2020.106883","volume":"1","author":"M. Mostafa S,","year":"2020","unstructured":"M. Mostafa S, (2020) Missing data imputation by the aid of features similarities. Int J Big Data Manag 1:81. https:\/\/doi.org\/10.1504\/IJBDM.2020.106883","journal-title":"Int J Big Data Manag"},{"key":"10601_CR6","doi-asserted-by":"publisher","first-page":"372","DOI":"10.1177\/1094428114548590","volume":"17","author":"DA Newman","year":"2014","unstructured":"Newman DA (2014) Missing data: five practical guidelines. Organ Res Methods 17:372\u2013411. https:\/\/doi.org\/10.1177\/1094428114548590","journal-title":"Organ Res Methods"},{"key":"10601_CR7","doi-asserted-by":"publisher","first-page":"4009","DOI":"10.32604\/cmc.2022.020261","volume":"70","author":"P Keerin","year":"2022","unstructured":"Keerin P, Boongoen T (2022) Improved KNN imputation for missing values in gene expression data. Comput Mater Contin 70:4009\u20134025. https:\/\/doi.org\/10.32604\/cmc.2022.020261","journal-title":"Comput Mater Contin"},{"key":"10601_CR8","doi-asserted-by":"publisher","first-page":"673","DOI":"10.3389\/fpsyt.2020.00673","volume":"11","author":"C-Y Cheng","year":"2020","unstructured":"Cheng C-Y, Tseng W-L, Chang C-F et al (2020) A deep learning approach for missing data imputation of rating scales assessing attention-deficit hyperactivity disorder. Front Psychiatry 11:673. https:\/\/doi.org\/10.3389\/fpsyt.2020.00673","journal-title":"Front Psychiatry"},{"key":"10601_CR9","doi-asserted-by":"publisher","first-page":"10321","DOI":"10.3390\/ijerph181910321","volume":"18","author":"C Xie","year":"2021","unstructured":"Xie C, Huang C, Zhang D, He W (2021) BiLSTM-I: a deep learning-based long interval gap-filling method for meteorological observation data. Int J Environ Res Public Health 18:10321. https:\/\/doi.org\/10.3390\/ijerph181910321","journal-title":"Int J Environ Res Public Health"},{"key":"10601_CR10","doi-asserted-by":"publisher","first-page":"5045","DOI":"10.3390\/s20185045","volume":"20","author":"W Song","year":"2020","unstructured":"Song W, Gao C, Zhao Y, Zhao Y (2020) A time series data filling method based on LSTM\u2014taking the stem moisture as an example. Sensors 20:5045. https:\/\/doi.org\/10.3390\/s20185045","journal-title":"Sensors"},{"issue":"12","key":"10601_CR11","doi-asserted-by":"publisher","first-page":"9071","DOI":"10.48550\/arXiv.2202.12441","volume":"35","author":"J Park","year":"2022","unstructured":"Park J, Muller J, Arora B et al (2022) Long-term missing value imputation for time series data using deep neural networks. Neural Comput Appl 35(12):9071\u20139091. https:\/\/doi.org\/10.48550\/arXiv.2202.12441","journal-title":"Neural Comput Appl"},{"key":"10601_CR12","doi-asserted-by":"crossref","unstructured":"Phan T-T-H (2020) Machine Learning for Univariate Time Series Imputation. In: 2020 international conference on multimedia analysis and pattern recognition (MAPR). IEEE, Ha Noi, Vietnam, pp 1\u20136","DOI":"10.1109\/MAPR49794.2020.9237768"},{"key":"10601_CR13","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1002\/mpr.329","volume":"20","author":"MJ Azur","year":"2011","unstructured":"Azur MJ, Stuart EA, Frangakis C, Leaf PJ (2011) Multiple imputation by chained equations: what is it and how does it work? Int J Methods Psychiatr Res 20:40\u201349. https:\/\/doi.org\/10.1002\/mpr.329","journal-title":"Int J Methods Psychiatr Res"},{"key":"10601_CR14","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/6668822","volume":"2021","author":"S Javadi","year":"2021","unstructured":"Javadi S, Bahrampour A, Saber MM et al (2021) Evaluation of four multiple imputation methods for handling missing binary outcome data in the presence of an interaction between a dummy and a continuous variable. J Probab Stat 2021:e6668822. https:\/\/doi.org\/10.1155\/2021\/6668822","journal-title":"J Probab Stat"},{"key":"10601_CR15","doi-asserted-by":"publisher","DOI":"10.1002\/met.1873","volume":"27","author":"E Afrifa-Yamoah","year":"2020","unstructured":"Afrifa-Yamoah E, Mueller UA, Taylor SM, Fisher AJ (2020) Missing data imputation of high-resolution temporal climate time series data. Meteorol Appl 27:e1873. https:\/\/doi.org\/10.1002\/met.1873","journal-title":"Meteorol Appl"},{"key":"10601_CR16","doi-asserted-by":"publisher","first-page":"1408","DOI":"10.1592\/phco.29.12.1408","volume":"29","author":"SB Woolley","year":"2009","unstructured":"Woolley SB, Cardoni AA, Goethe JW (2009) Last-observation-carried-forward imputation method in clinical efficacy trials: review of 352 antidepressant studies. Pharmacother J Hum Pharmacol Drug Ther 29:1408\u20131416. https:\/\/doi.org\/10.1592\/phco.29.12.1408","journal-title":"Pharmacother J Hum Pharmacol Drug Ther"},{"key":"10601_CR17","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1016\/j.jhydrol.2011.02.031","volume":"402","author":"J Est\u00e9vez","year":"2011","unstructured":"Est\u00e9vez J, Gavil\u00e1n P, Gir\u00e1ldez JV (2011) Guidelines on validation procedures for meteorological data from automatic weather stations. J Hydrol 402:144\u2013154","journal-title":"J Hydrol"},{"key":"10601_CR18","doi-asserted-by":"publisher","first-page":"2775","DOI":"10.1016\/j.apenergy.2011.01.070","volume":"88","author":"M Zarzo","year":"2011","unstructured":"Zarzo M, Mart\u00ed P (2011) Modeling the variability of solar radiation data among weather stations by means of principal components analysis. Appl Energy 88:2775\u20132784","journal-title":"Appl Energy"},{"key":"10601_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-642-61132-2_1","volume":"4","author":"R Snyder","year":"1996","unstructured":"Snyder R, Brown P, Hubbard K, Meyer S (1996) A guide to automated weather station networks in North America. Adv Bioclimatol 4:1\u201361","journal-title":"Adv Bioclimatol"},{"key":"10601_CR20","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1016\/j.agsy.2005.07.004","volume":"88","author":"M Rivington","year":"2006","unstructured":"Rivington M, Matthews K, Bellocchi G, Buchan K (2006) Evaluating uncertainty introduced to process-based simulation model estimates by alternative sources of meteorological data. Agric Syst 88:451\u2013471","journal-title":"Agric Syst"},{"key":"10601_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2021\/8836806","volume":"2021","author":"S Osah","year":"2021","unstructured":"Osah S, Acheampong AA, Fosu C, Dadzie I (2021) Evaluation of zenith tropospheric delay derived from ray-traced VMF3 product over the west African region using GNSS observations. Adv Meteorol 2021:1\u201314","journal-title":"Adv Meteorol"},{"key":"10601_CR22","doi-asserted-by":"publisher","first-page":"900","DOI":"10.1016\/j.asr.2019.05.027","volume":"64","author":"RC Ssenyunzi","year":"2019","unstructured":"Ssenyunzi RC, Oruru B, D\u2019ujanga FM, et al (2019) Variability and accuracy of Zenith Total Delay over the East African tropical region. Adv Space Res 64:900\u2013920","journal-title":"Adv Space Res"},{"key":"10601_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2015\/723071","volume":"2015","author":"OA Isioye","year":"2015","unstructured":"Isioye OA, Combrinck L, Botai JO, Munghemezulu C (2015) The potential for observing African weather with GNSS remote sensing. Adv Meteorol 2015:1\u201316","journal-title":"Adv Meteorol"},{"key":"10601_CR24","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1016\/j.atmosres.2013.08.007","volume":"144","author":"M Colli","year":"2014","unstructured":"Colli M, Lanza L, La Barbera P, Chan P (2014) Measurement accuracy of weighing and tipping-bucket rainfall intensity gauges under dynamic laboratory testing. Atmospheric Res 144:186\u2013194","journal-title":"Atmospheric Res"},{"key":"10601_CR25","doi-asserted-by":"publisher","first-page":"1274","DOI":"10.1049\/iet-rpg.2016.1043","volume":"11","author":"H Sangrody","year":"2017","unstructured":"Sangrody H, Sarailoo M, Zhou N et al (2017) Weather forecasting error in solar energy forecasting. IET Renew Power Gener 11:1274\u20131280","journal-title":"IET Renew Power Gener"},{"key":"10601_CR26","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1115\/1.1862266","volume":"127","author":"M Lange","year":"2005","unstructured":"Lange M (2005) On the uncertainty of wind power predictions\u2014Analysis of the forecast accuracy and statistical distribution of errors. J Sol Energy Eng 127:177\u2013184","journal-title":"J Sol Energy Eng"},{"key":"10601_CR27","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1007\/BF02478259","volume":"5","author":"WS McCulloch","year":"1943","unstructured":"McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5:115\u2013133. https:\/\/doi.org\/10.1007\/BF02478259","journal-title":"Bull Math Biophys"},{"key":"10601_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/J.CHNAES.2022.09.004","author":"P Hunasigi","year":"2022","unstructured":"Hunasigi P, Jedhe S, Mane M, Patil-Shinde V (2022) Multilayer perceptron neural network based models for prediction of the rainfall and reference crop evapotranspiration for sub-humid climate of Dapoli, Ratnagiri District, India. Acta Ecol Sin. https:\/\/doi.org\/10.1016\/J.CHNAES.2022.09.004","journal-title":"Acta Ecol Sin"},{"key":"10601_CR29","doi-asserted-by":"publisher","first-page":"26","DOI":"10.9781\/ijimai.2016.415","volume":"4","author":"H Ramchoun","year":"2016","unstructured":"Ramchoun H, Amine M, Idrissi J et al (2016) Multilayer perceptron: architecture optimization and training. Int J Interact Multimed Artif Intell 4:26. https:\/\/doi.org\/10.9781\/ijimai.2016.415","journal-title":"Int J Interact Multimed Artif Intell"},{"key":"10601_CR30","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9:1735\u20131780. https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","journal-title":"Neural Comput"},{"key":"10601_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2021.107371","volume":"157","author":"J Mejia","year":"2021","unstructured":"Mejia J, Avelar-Sosa L, Mederos B et al (2021) Prediction of time series using an analysis filter bank of LSTM units. Comput Ind Eng 157:107371. https:\/\/doi.org\/10.1016\/j.cie.2021.107371","journal-title":"Comput Ind Eng"},{"key":"10601_CR32","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","volume":"1","author":"Y LeCun","year":"1989","unstructured":"LeCun Y, Boser B, Denker JS et al (1989) Backpropagation applied to handwritten zip code recognition. Neural Comput 1:541\u2013551. https:\/\/doi.org\/10.1162\/neco.1989.1.4.541","journal-title":"Neural Comput"},{"key":"10601_CR33","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1186\/s40537-021-00444-8","volume":"8","author":"L Alzubaidi","year":"2021","unstructured":"Alzubaidi L, Zhang J, Humaidi AJ et al (2021) Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J Big Data 8:53. https:\/\/doi.org\/10.1186\/s40537-021-00444-8","journal-title":"J Big Data"},{"key":"10601_CR34","doi-asserted-by":"publisher","first-page":"3032","DOI":"10.3390\/w12113032","volume":"12","author":"L Dong","year":"2020","unstructured":"Dong L, Fang D, Wang X et al (2020) Prediction of streamflow based on dynamic sliding window LSTM. Water 12:3032. https:\/\/doi.org\/10.3390\/w12113032","journal-title":"Water"},{"key":"10601_CR35","volume-title":"Deep Learning","author":"I Goodfellow","year":"2016","unstructured":"Goodfellow I, Bengio Y, Courville A (2016) Deep Learning. MIT Press"},{"key":"10601_CR36","doi-asserted-by":"publisher","first-page":"2133","DOI":"10.1080\/01431160802549278","volume":"30","author":"D Stathakis","year":"2009","unstructured":"Stathakis D (2009) How many hidden layers and nodes? Int J Remote Sens 30:2133\u20132147. https:\/\/doi.org\/10.1080\/01431160802549278","journal-title":"Int J Remote Sens"},{"key":"10601_CR37","unstructured":"Heaton J (2008) Introduction to Neural Networks with Java. Heaton Research, Inc"},{"key":"10601_CR38","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.724","author":"MIC Rachmatullah","year":"2021","unstructured":"Rachmatullah MIC, Santoso J, Surendro K (2021) Determining the number of hidden layer and hidden neuron of neural network for wind speed prediction. PeerJ Comput Sci. https:\/\/doi.org\/10.7717\/peerj-cs.724","journal-title":"PeerJ Comput Sci"},{"key":"10601_CR39","doi-asserted-by":"publisher","first-page":"527","DOI":"10.1016\/0169-2070(93)90079-3","volume":"9","author":"S Makridakis","year":"1993","unstructured":"Makridakis S (1993) Accuracy measures: theoretical and practical concerns. Int J Forecast 9:527\u2013529. https:\/\/doi.org\/10.1016\/0169-2070(93)90079-3","journal-title":"Int J Forecast"},{"key":"10601_CR40","unstructured":"Triebe O, Hewamalage H, Pilyugina P, et al (2021) NeuralProphet: Explainable Forecasting at Scale"},{"key":"10601_CR41","unstructured":"Triebe O, Laptev N, Rajagopal R (2019) AR-Net: A simple Auto-Regressive Neural Network for time-series"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10601-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-10601-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10601-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T05:22:58Z","timestamp":1759209778000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-10601-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,6]]},"references-count":41,"journal-issue":{"issue":"29","published-print":{"date-parts":[[2025,10]]}},"alternative-id":["10601"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-10601-8","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,6]]},"assertion":[{"value":"19 February 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 October 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 December 2024","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 declared that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The authors do not have permission to share data.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Data availability"}}]}}