{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T05:28:29Z","timestamp":1774416509994,"version":"3.50.1"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"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":[[2026,3]]},"DOI":"10.1007\/s00521-025-11803-4","type":"journal-article","created":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T02:14:32Z","timestamp":1772504072000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhanced data quality-driven framework using stacked-GRU for robust prediction of volatile crypto trends"],"prefix":"10.1007","volume":"38","author":[{"given":"Muhammad","family":"Wajid","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5569-5629","authenticated-orcid":false,"given":"Ahmad Kamran","family":"Malik","sequence":"additional","affiliation":[]},{"given":"Nafees","family":"Qamar","sequence":"additional","affiliation":[]},{"given":"Muneeba","family":"Islam","sequence":"additional","affiliation":[]},{"given":"Iftikhar Azim","family":"Niaz","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,3,3]]},"reference":[{"key":"11803_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115480","volume":"184","author":"H Kim","year":"2021","unstructured":"Kim H, Bock G, Lee G (2021) Predicting Ethereum prices with ML based on Blockchain information. Expert Syst Appl 184:115480","journal-title":"Expert Syst Appl"},{"key":"11803_CR2","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1016\/j.irfa.2018.09.003","volume":"62","author":"S Corbet","year":"2019","unstructured":"Corbet S, Lucey B, Urquhart A, Yarovaya L (2019) International review of financial analysis cryptocurrencies as a financial asset: a systematic analysis. Int Rev Financ Analysisvol 62:182\u2013199","journal-title":"Int Rev Financ Analysisvol"},{"issue":"01","key":"11803_CR3","first-page":"140","volume":"34","author":"V Derbentsev","year":"2021","unstructured":"Derbentsev V, Babenko V, Khrustalev K, Obruch H, Khrustalova S (2021) Comparative performance of machine learning ensemble algorithms for forecasting cryptocurrency prices. Int J Eng 34(01):140\u2013148","journal-title":"Int J Eng"},{"key":"11803_CR4","doi-asserted-by":"crossref","unstructured":"Zhang Z, Dai H, Zhou J, Kumar S, Mart\u00c3\u00adnez M, Wang H, (2021) Forecasting cryptocurrency price using convolutional neural networks with weighted and attentive memory channels. Expert Syst Appl 183:115378","DOI":"10.1016\/j.eswa.2021.115378"},{"key":"11803_CR5","doi-asserted-by":"crossref","unstructured":"Mujlid H (2023) A survey on machine learning approaches in cryptocurrency: challenges and opportunities. In: 2023 4th International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), pp 1\u20136","DOI":"10.1109\/iCoMET57998.2023.10099130"},{"issue":"7","key":"11803_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s43546-021-00090-5","volume":"1","author":"P Kayal","year":"2021","unstructured":"Kayal P, Rohilla P (2021) Bitcoin in the economics and finance literature: a survey. SN Bus Econ 1(7):1\u201321","journal-title":"SN Bus Econ"},{"key":"11803_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.ribaf.2022.101799","volume":"63","author":"Y Ren","year":"2022","unstructured":"Ren Y, Ma C, Kong X, Baltas K (2022) Past, present, and future of the application of machine learning in cryptocurrency research. Res Int Bus Financ 63:101799","journal-title":"Res Int Bus Financ"},{"key":"11803_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.frl.2021.102139","volume":"45","author":"B Jia","year":"2022","unstructured":"Jia B, Goodell JW, Shen D (2022) Momentum or reversal: which is the appropriate third factor for cryptocurrencies? Financ Res Lett 45:102139","journal-title":"Financ Res Lett"},{"key":"11803_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbef.2021.100577","volume":"32","author":"JW Goodell","year":"2021","unstructured":"Goodell JW, Kumar S, Marc W, Pattnaik D (2021) Artificial intelligence and machine learning in finance: identifying foundations, themes, and research clusters from bibliometric analysis. J Behav Exp Financ 32:100577","journal-title":"J Behav Exp Financ"},{"key":"11803_CR10","doi-asserted-by":"crossref","unstructured":"W\u0105torek M, Drozdz S, Kwapien J (2021) Multiscale characteristics of the emerging global cryptocurrency market. Phys. Rep., vol. 901, pp 1\u201382","DOI":"10.1016\/j.physrep.2020.10.005"},{"key":"11803_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2020.124569","volume":"551","author":"R Chowdhury","year":"2020","unstructured":"Chowdhury R, Rahman MA, Rahman MS, Mahdy MRC (2020) An approach to predict and forecast the price of constituents and index of cryptocurrency using machine learning. Phys A 551:124569","journal-title":"Phys A"},{"key":"11803_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbef.2020.100335","volume":"27","author":"D Aggarwal","year":"2020","unstructured":"Aggarwal D, Chandrasekaran S, Annamalai B (2020) A complete empirical ensemble mode decomposition and support vector machine-based approach to predict Bitcoin prices. J Behav Exp Financ 27:100335","journal-title":"J Behav Exp Financ"},{"key":"11803_CR13","first-page":"1","volume":"330","author":"E Akyildirim","year":"2021","unstructured":"Akyildirim E, Cepni O, Corbet S, Uddin GS (2021) Forecasting mid-price movement of Bitcoin futures using machine learning. Ann Oper Res 330:1\u201323","journal-title":"Ann Oper Res"},{"key":"11803_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2022.109584","volume":"129","author":"S Rajabi","year":"2022","unstructured":"Rajabi S, Roozkhosh P, Motahari N (2022) MLP-based learnable window size for bitcoin price prediction. Appl Soft Comput 129:109584","journal-title":"Appl Soft Comput"},{"key":"11803_CR15","doi-asserted-by":"crossref","unstructured":"Nithiya S (2023) Bitcoin Price Prediction Using Machine Learning. In: 2023 International Conference on Computer Communication and Informatics, pp 1\u20136","DOI":"10.1109\/ICCCI56745.2023.10128307"},{"key":"11803_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.frl.2018.12.032","volume":"32","author":"S Xiaolei","year":"2020","unstructured":"Xiaolei S, Mingxi L, Zeqian S (2020) A novel cryptocurrency price trend forecasting model based on LightGBM. Financ Res Lett 32:101084","journal-title":"Financ Res Lett"},{"key":"11803_CR17","volume":"9","author":"S Abul","year":"2022","unstructured":"Abul S, Sadorsky P (2022) Machine learning with applications forecasting bitcoin price direction with random forests: how important are interest rates, inflation, and market volatility? Mach Learn with Appl 9:100355","journal-title":"Mach Learn with Appl"},{"key":"11803_CR18","doi-asserted-by":"publisher","first-page":"596","DOI":"10.1016\/j.asoc.2018.11.038","volume":"75","author":"DCA Mallqui","year":"2019","unstructured":"Mallqui DCA, Fernandes RAS (2019) Predicting the direction, maximum, minimum and closing prices of daily Bitcoin exchange rate using machine learning techniques. Appl Soft Comput J 75:596\u2013606","journal-title":"Appl Soft Comput J"},{"key":"11803_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.datak.2022.102009","volume":"139","author":"I Nasirtafreshi","year":"2022","unstructured":"Nasirtafreshi I (2022) Forecasting cryptocurrency prices using recurrent neural network and long short-term memory. Data Knowl Eng 139:102009","journal-title":"Data Knowl Eng"},{"key":"11803_CR20","first-page":"2126518","volume":"1","author":"C Luo","year":"2022","unstructured":"Luo C, Pan L, Chen B, Xu H (2022) Bitcoin price forecasting: an integrated approach using hybrid LSTM-ELM models. Math Probl Eng 1:2126518","journal-title":"Math Probl Eng"},{"issue":"1","key":"11803_CR21","doi-asserted-by":"publisher","first-page":"51","DOI":"10.3390\/jrfm16010051","volume":"16","author":"J Chen","year":"2023","unstructured":"Chen J (2023) Analysis of bitcoin price prediction using machine learning. J Risk Financ Manag 16(1):51\u201361","journal-title":"J Risk Financ Manag"},{"key":"11803_CR22","first-page":"62","volume":"10","author":"G Singh","year":"2023","unstructured":"Singh G, Thanaya BI (2023) Bitcoin price prediction and recommendation system using deep learning techniques and twitter sentiment analysis. Int Res J Eng Technol (IRJET) 10:62\u201367","journal-title":"Int Res J Eng Technol (IRJET)"},{"issue":"28","key":"11803_CR23","doi-asserted-by":"publisher","first-page":"22979","DOI":"10.1007\/s00521-020-05129-6","volume":"37","author":"M Mudassir","year":"2020","unstructured":"Mudassir M, Bennbaia S, Unal D (2020) Time-series forecasting of Bitcoin prices using high-dimensional features: a machine learning approach. Neural Comput Appl 37(28):22979\u201322993","journal-title":"Neural Comput Appl"},{"key":"11803_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.121012","volume":"234","author":"M Khosravi","year":"2023","unstructured":"Khosravi M, Ghazani MM (2023) Novel insights into the modeling financial time-series through machine learning methods: evidence from the cryptocurrency market. Expert Syst Appl 234:121012","journal-title":"Expert Syst Appl"},{"key":"11803_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2023.113955","volume":"169","author":"C Zhong","year":"2023","unstructured":"Zhong C, Du W, Xu W, Huang Q, Zhao Y, Wang M (2023) LSTM-ReGAT: a network-centric approach for cryptocurrency price trend prediction. Decis Support Syst 169:113955","journal-title":"Decis Support Syst"},{"key":"11803_CR26","doi-asserted-by":"crossref","unstructured":"Hamayel MJ (2021) A novel cryptocurrency price prediction model using GRU, LSTM and bi-LSTM machine learning algorithms. Ai, vol. 2: pp. 477\u2013496","DOI":"10.3390\/ai2040030"},{"key":"11803_CR27","doi-asserted-by":"crossref","unstructured":"Seabe PL, Rodrigue C, Moutsinga B, Pindza E (2023) Forecasting cryptocurrency prices using LSTM , GRU , and Bi-Directional LSTM: a deep learning approach, AI, vol. 2, pp 1\u201318","DOI":"10.3390\/fractalfract7020203"},{"key":"11803_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.119233","volume":"213","author":"AA Oyedele","year":"2023","unstructured":"Oyedele AA, Ajayi AO, Oyedele LO, Bello SA, Jimoh KO (2023) Performance evaluation of deep learning and boosted trees for cryptocurrency closing price prediction. Expert Syst Appl 213:119233","journal-title":"Expert Syst Appl"},{"key":"11803_CR29","doi-asserted-by":"crossref","first-page":"11","DOI":"10.3390\/data7010011","volume":"7","author":"S Gated","year":"2022","unstructured":"Gated S, Unit R (2022) Cryptocurrency price prediction with convolutional neural network and stack gated recurrent unit. Data 7:11","journal-title":"Data"},{"key":"11803_CR30","doi-asserted-by":"crossref","unstructured":"Kazeminia S, Sajedi H, Arjmand M (2023) Real-time bitcoin price prediction using hybrid 2D- CNN LSTM model. In: 2023 9th International Conference on Web Research (ICWR) IEEE.pp 173\u2013178","DOI":"10.1109\/ICWR57742.2023.10139275"},{"issue":"2020","key":"11803_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.frl.2020.101755","volume":"40","author":"M Liu","year":"2021","unstructured":"Liu M, Li G, Li J, Zhu X, Yao Y (2021) Forecasting the price of Bitcoin using deep learning. Financ Res Lett 40(2020):101755","journal-title":"Financ Res Lett"},{"key":"11803_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2020.122542","volume":"272","author":"Y Liu","year":"2020","unstructured":"Liu Y, Chen H, Wang X, Wu X (2020) Energy consumption prediction and diagnosis of public buildings based on support vector machine learning: a case study in China. J Clean Prod 272:122542","journal-title":"J Clean Prod"},{"issue":"4","key":"11803_CR33","doi-asserted-by":"publisher","first-page":"765","DOI":"10.1007\/s00466-023-02317-8","volume":"72","author":"S Balkrishna","year":"2023","unstructured":"Balkrishna S, Marcus T (2023) Recurrent and convolutional neural networks in structural dynamics: a modified attention steered encoder - decoder architecture versus LSTM versus GRU versus TCN topologies to predict the response of shock wave-loaded plates. Comput Mech 72(4):765\u2013786","journal-title":"Comput Mech"},{"issue":"2021","key":"11803_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.dajour.2022.100071","volume":"3","author":"M Bansal","year":"2022","unstructured":"Bansal M, Goyal A, Choudhary A (2022) A comparative analysis of K-nearest neighbor, genetic, support vector machine, decision tree, and long short term memory algorithms in machine learning. Decis Anal J 3(2021):100071","journal-title":"Decis Anal J"},{"issue":"18","key":"11803_CR35","first-page":"5143","volume":"20","author":"BC Recurrent","year":"2023","unstructured":"Recurrent BC, Mechanism A (2023) GPS trajectory completion using end-to-end bidirectional convolutional recurrent. Sensors 20(18):5143","journal-title":"Sensors"},{"key":"11803_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2023.126665","volume":"268","author":"L Wu","year":"2023","unstructured":"Wu L, Zhang Y (2023) Attention-based encoder-decoder networks for state of charge estimation of lithium-ion battery. Energy 268:126665","journal-title":"Energy"},{"issue":"2","key":"11803_CR37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12046-023-02126-y","volume":"48","author":"GK Sahoo","year":"2023","unstructured":"Sahoo GK, Das SK, Singh P (2023) Two layered gated recurrent Stack long short-term memory networks for driver \u2019 s behavior analysis. S\u0101dhan\u0101 48(2):1\u201318","journal-title":"S\u0101dhan\u0101"},{"key":"11803_CR38","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1016\/j.ins.2020.05.035","volume":"541","author":"X Liu","year":"2020","unstructured":"Liu X et al (2020) Attention-based bidirectional GRU networks for efficient HTTPS traffic classification. Inf Sci (Ny) 541:297\u2013315","journal-title":"Inf Sci (Ny)"},{"key":"11803_CR39","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1016\/j.neucom.2020.07.026","volume":"414","author":"L Zeng","year":"2020","unstructured":"Zeng L, Ren W, Shan L (2020) Neurocomputing attention-based bidirectional gated recurrent unit neural networks for well logs prediction and lithology identification q. Neurocomputing 414:153\u2013171","journal-title":"Neurocomputing"},{"key":"11803_CR40","doi-asserted-by":"crossref","unstructured":"Yang S, Yu X, Zhou Y (2020) LSTM and GRU neural network performance comparison study. In: 2020 International workshop on electronic communication and artificial intelligence (IWECAI) pp 98\u2013101","DOI":"10.1109\/IWECAI50956.2020.00027"},{"issue":"13","key":"11803_CR41","doi-asserted-by":"publisher","first-page":"9803","DOI":"10.1007\/s00521-023-08218-4","volume":"35","author":"T Wadhera","year":"2023","unstructured":"Wadhera T, Bedi J, Sharma S (2023) Autism spectrum disorder prediction using bidirectional Stack gated recurrent unit with time-distributor wrapper: an EEG study. Neural Comput Appl 35(13):9803\u20139818","journal-title":"Neural Comput Appl"},{"key":"11803_CR42","volume":"3","author":"R Al-shabandar","year":"2021","unstructured":"Al-shabandar R, Jaddoa A, Liatsis P, Jaafar A (2021) Machine learning with applications. Mach Learn with Appl 3:100013","journal-title":"Mach Learn with Appl"},{"key":"11803_CR43","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1016\/j.eswa.2023.121401","volume":"237","author":"E Koo","year":"2024","unstructured":"Koo E, Kim G (2024) Centralized decomposition approach in LSTM for Bitcoin price prediction. Expert Syst Appl 237:121\u2013131","journal-title":"Expert Syst Appl"},{"key":"11803_CR44","doi-asserted-by":"crossref","unstructured":"Kanaparthi V (2024) Robustness evaluation of LSTM-based deep learning models for bitcoin price prediction in the presence of random disturbances","DOI":"10.21203\/rs.3.rs-3906529\/v1"},{"key":"11803_CR45","doi-asserted-by":"crossref","unstructured":"Cheng J et al (2024) Forecasting Bitcoin prices using artificial intelligence: combination of ML, SARIMA, and Facebook Prophet models. Technol Forecast Soc Chang 198:122\u2013131","DOI":"10.1016\/j.techfore.2023.122938"},{"issue":"06","key":"11803_CR46","doi-asserted-by":"publisher","first-page":"1981","DOI":"10.4236\/tel.2019.96126","volume":"9","author":"I Roussou","year":"2019","unstructured":"Roussou I, Dritsaki C, Stiakakis E (2019) The Bitcoin\u2019s network effects paradox-a time series analysis. Theoret Econ Lett 9(06):1981\u20132001","journal-title":"Theoret Econ Lett"},{"issue":"2","key":"11803_CR47","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1108\/JMLC-12-2017-0074","volume":"22","author":"C Albrecht","year":"2019","unstructured":"Albrecht C, Duffin KM, Hawkins S, Morales Rocha VM (2019) The use of cryptocurrencies in the money laundering process. J Money Launder Control 22(2):210\u2013216","journal-title":"J Money Launder Control"},{"key":"11803_CR48","unstructured":"CoinMarketCap. Bitcoin (BTC) Price, Charts, and News. Available online: https:\/\/coinmarketcap.com\/currencies\/bitcoin\/ Accessed on 27 Apr 2025"},{"key":"11803_CR49","doi-asserted-by":"crossref","unstructured":"Chennupati A, Prahas B, Ghali BA, Jasvitha BD, Murali K (2024) Comparative analysis of bitcoin price prediction models: LSTM, BiLSTM, ARIMA and Transformers. In: 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT), pp. 1-7. IEEE","DOI":"10.1109\/ICCCNT61001.2024.10724247"},{"key":"11803_CR50","doi-asserted-by":"crossref","unstructured":"Mai T, Cavazza M, Prendinger H (2024) Transformer models for bitcoin price prediction. In: Proceedings of the 2024 3rd International Conference on Frontiers of Artificial Intelligence and Machine Learning, pp 236-241","DOI":"10.1145\/3653644.3680499"},{"key":"11803_CR51","unstructured":"Lundberg SM, Lee, S-I (2017) A unified approach to interpreting model predictions. Adv Neural Inform Process Syst vol. 30"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11803-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-025-11803-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11803-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T04:48:48Z","timestamp":1774414128000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-025-11803-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3]]},"references-count":51,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2026,3]]}},"alternative-id":["11803"],"URL":"https:\/\/doi.org\/10.1007\/s00521-025-11803-4","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3]]},"assertion":[{"value":"19 November 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 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 that they have no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}],"article-number":"97"}}