{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T14:21:50Z","timestamp":1783952510545,"version":"3.55.0"},"reference-count":50,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100013139","name":"Humanities and Social Science Fund of Ministry of Education","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100013139","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003819","name":"Hubei Province Natural Science Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003819","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Information Sciences"],"published-print":{"date-parts":[[2026,11]]},"DOI":"10.1016\/j.ins.2026.123881","type":"journal-article","created":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T23:31:25Z","timestamp":1783639885000},"page":"123881","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Considering mixed-frequency data and multiple interactions for hard disk drive failure prediction: An integrated grey system framework"],"prefix":"10.1016","volume":"756","author":[{"given":"Qinzi","family":"Xiao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3711-5146","authenticated-orcid":false,"given":"Mingyun","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Congjun","family":"Rao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.ins.2026.123881_b0005","doi-asserted-by":"crossref","DOI":"10.1016\/j.compeleceng.2024.109195","article-title":"Remaining lifespan prediction on multiple types of hard disks under conditions of data imbalance","volume":"116","author":"Wang","year":"2024","journal-title":"Comput. Electr. Eng."},{"key":"10.1016\/j.ins.2026.123881_b0010","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.107339","article-title":"Cost aware LSTM model for predicting hard disk drive failures based on extremely imbalanced SMART sensors data","volume":"127","author":"Ahmed","year":"2024","journal-title":"Eng. Appl. Artif. Intel."},{"key":"10.1016\/j.ins.2026.123881_b0015","doi-asserted-by":"crossref","first-page":"148","DOI":"10.3390\/fractalfract9030148","article-title":"A new approach based on metaheuristic optimization using chaotic functional connectivity matrices and fractal dimension analysis for AI-driven detection of orthodontic growth and development stage","volume":"9","author":"Cicek","year":"2025","journal-title":"Fractal Fract."},{"key":"10.1016\/j.ins.2026.123881_b0020","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1016\/j.isatra.2024.12.023","article-title":"End-to-end multi-scale residual network with parallel attention mechanism for fault diagnosis under noise and small samples","volume":"157","author":"Sun","year":"2025","journal-title":"ISA Trans."},{"key":"10.1016\/j.ins.2026.123881_b0025","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1007\/s13042-026-03115-3","article-title":"Dynamic evolution-driven domain adaptation with wavelet dual-path structure for cross-domain fault diagnosis","volume":"17","author":"Shen","year":"2026","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"10.1016\/j.ins.2026.123881_b0030","article-title":"Predicting severely imbalanced data disk drive failures with machine learning models","volume":"9","author":"Ahmed","year":"2022","journal-title":"Machine Learning Appl."},{"key":"10.1016\/j.ins.2026.123881_b0035","doi-asserted-by":"crossref","DOI":"10.3389\/fcomp.2024.1400943","article-title":"A survivability analysis of enterprise hard drives incorporating the impact of workload","volume":"6","author":"Mallik","year":"2024","journal-title":"Front. Computer Sci."},{"key":"10.1016\/j.ins.2026.123881_b0040","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1016\/j.apm.2024.04.025","article-title":"New Weibull Log-Logistic grey forecasting model for a hard disk drive failures","volume":"131","author":"Chen","year":"2024","journal-title":"App. Math. Model."},{"key":"10.1016\/j.ins.2026.123881_b0045","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2025.103779","article-title":"Open-set classification method via latent representation prompt and time\u2013frequency fusion toward unknown fault recognition","volume":"68","author":"Sun","year":"2025","journal-title":"Adv. Eng. Inf."},{"issue":"5","key":"10.1016\/j.ins.2026.123881_b0050","first-page":"783","article-title":"Machine learning methods for predicting failures in hard drives: a multiple-instance application","volume":"6","author":"Murray","year":"2005","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.ins.2026.123881_b0055","doi-asserted-by":"crossref","unstructured":"V. Tomer, V. Sharma, S. Gupta, D.P. Singh, Hard disk drive failure prediction using SMART attribute, Materials Today: Proceedings, 46 (2021) 11258-11262.","DOI":"10.1016\/j.matpr.2021.03.229"},{"issue":"2","key":"10.1016\/j.ins.2026.123881_b0060","doi-asserted-by":"crossref","first-page":"1569","DOI":"10.1007\/s13204-021-02039-4","article-title":"Disk storage failure prediction in datacenter using machine learning models","volume":"13","author":"Ramanathan","year":"2023","journal-title":"Appl. Nanosci."},{"issue":"1","key":"10.1016\/j.ins.2026.123881_b0065","first-page":"69","article-title":"Deep learning for HDD health assessment: an application based on LSTM","volume":"71","author":"De","year":"2020","journal-title":"IEEE Trans. Comput."},{"key":"10.1016\/j.ins.2026.123881_b0070","doi-asserted-by":"crossref","unstructured":"A. Galli, V. Moscato, G. Sperl\u00ed, A.D. Santo, et al., An explainable artificial intelligence methodology for hard disk fault prediction, International Conference on Database and Expert Systems Applications, Cham: Springer International Publishing, (2020) 403-413.","DOI":"10.1007\/978-3-030-59003-1_26"},{"key":"10.1016\/j.ins.2026.123881_b0075","article-title":"Multi-instance deep learning based on attention mechanism for failure prediction of unlabeled hard disk drives","volume":"70","author":"Wang","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"5","key":"10.1016\/j.ins.2026.123881_b0080","doi-asserted-by":"crossref","first-page":"2890","DOI":"10.1109\/TSC.2024.3394692","article-title":"SiaDFP: a disk failure prediction framework based on siamese neural network in large-scale data center","volume":"17","author":"Fang","year":"2024","journal-title":"IEEE Trans. Serv. Comput."},{"issue":"61","key":"10.1016\/j.ins.2026.123881_b0085","article-title":"FE-GRN neural network in HDD failure to optimize detection for enhanced reliability","volume":"7","author":"Hai","year":"2025","journal-title":"IEEE Trans. Magn."},{"key":"10.1016\/j.ins.2026.123881_b0090","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1016\/j.isatra.2024.02.023","article-title":"A novel mixed frequency sampling discrete grey model for forecasting hard disk drive failure","volume":"147","author":"Chen","year":"2024","journal-title":"ISA Trans."},{"key":"10.1016\/j.ins.2026.123881_b0095","series-title":"The MIDAS touch: mixed data sampling regression models","author":"Ghysels","year":"2004"},{"issue":"4","key":"10.1016\/j.ins.2026.123881_b0100","doi-asserted-by":"crossref","DOI":"10.1016\/j.ecosys.2023.101131","article-title":"Mixed-frequency Growth-at-Risk with the MIDAS-QR method: evidence from China","volume":"47","author":"Xu","year":"2023","journal-title":"Econ. Syst."},{"key":"10.1016\/j.ins.2026.123881_b0105","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1016\/j.iref.2022.06.025","article-title":"Pricing VIX futures with mixed frequency macroeconomic information","volume":"93","author":"Yang","year":"2024","journal-title":"Int. Rev. Econ. Finance"},{"key":"10.1016\/j.ins.2026.123881_b0110","doi-asserted-by":"crossref","DOI":"10.1016\/j.frl.2023.104714","article-title":"Stock market volatility and economic policy uncertainty: New insight into a dynamic threshold mixed-frequency model","volume":"59","author":"Zeng","year":"2024","journal-title":"Financ. Res. Lett."},{"issue":"5","key":"10.1016\/j.ins.2026.123881_b0115","doi-asserted-by":"crossref","first-page":"1048","DOI":"10.1111\/obes.12555","article-title":"A mixed frequency BVAR for the euro area labour market","volume":"85","author":"Consolo","year":"2023","journal-title":"Oxf. Bull. Econ. Stat."},{"key":"10.1016\/j.ins.2026.123881_b0120","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.110674","article-title":"Explainable time series features for hard disk drive failure prediction","volume":"152","author":"Li","year":"2025","journal-title":"Eng. Appl. Artif. Intel."},{"key":"10.1016\/j.ins.2026.123881_b0125","doi-asserted-by":"crossref","DOI":"10.1016\/j.annals.2024.103887","article-title":"Forecast by mixed-frequency dynamic panel model","volume":"110","author":"Liu","year":"2025","journal-title":"Ann. Tour. Res."},{"key":"10.1016\/j.ins.2026.123881_b0130","doi-asserted-by":"crossref","DOI":"10.1016\/j.econmod.2025.107160","article-title":"Policy impact on the global COVID-19 pandemic and unemployment outcomes: a large-scale mixed frequency spatial approach","volume":"151","author":"Zhang","year":"2025","journal-title":"Econ. Model."},{"key":"10.1016\/j.ins.2026.123881_b0135","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.cjche.2025.02.025","article-title":"A multi-source mixed-frequency information fusion framework based on spatial\u2013temporal graph attention network for anomaly detection of catalyst loss in FCC regenerators","volume":"84","author":"Zhu","year":"2025","journal-title":"Chin. J. Chem. Eng."},{"key":"10.1016\/j.ins.2026.123881_b0140","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2022.118756","article-title":"Probability density forecasts for natural gas demand in China: do mixed-frequency dynamic factors matter?","volume":"312","author":"Ding","year":"2022","journal-title":"Appl. Energy"},{"issue":"5","key":"10.1016\/j.ins.2026.123881_b0145","doi-asserted-by":"crossref","first-page":"4241","DOI":"10.15244\/pjoes\/131856","article-title":"Forecasting China's Steam coal prices using dynamic factors and mixed-frequency data","volume":"30","author":"Wang","year":"2021","journal-title":"Pol. J. Environ. Stud."},{"issue":"3","key":"10.1016\/j.ins.2026.123881_b0150","doi-asserted-by":"crossref","first-page":"1206","DOI":"10.1016\/j.ijforecast.2023.10.009","article-title":"Reservoir computing for macroeconomic forecasting with mixed-frequency data","volume":"40","author":"Ballarin","year":"2024","journal-title":"Int. J. Forecast."},{"issue":"9","key":"10.1016\/j.ins.2026.123881_b0155","doi-asserted-by":"crossref","first-page":"2519","DOI":"10.1007\/s13042-022-01541-7","article-title":"A temporal-attribute attention neural network for mixed frequency data forecasting","volume":"13","author":"Wu","year":"2022","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"10.1016\/j.ins.2026.123881_b0160","doi-asserted-by":"crossref","DOI":"10.1016\/j.tourman.2024.105004","article-title":"Tourism forecasting by mixed-frequency machine learning","volume":"106","author":"Hu","year":"2025","journal-title":"Tour. Manag."},{"key":"10.1016\/j.ins.2026.123881_b0165","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.128300","article-title":"A novel carbon price forecasting model integrating mixed-frequency modeling into the transformer architecture from a multi-factor perspective","volume":"289","author":"Ji","year":"2025","journal-title":"Expert Syst. Appl."},{"issue":"3","key":"10.1016\/j.ins.2026.123881_b0170","doi-asserted-by":"crossref","first-page":"1122","DOI":"10.1016\/j.ijforecast.2022.05.005","article-title":"Mixed-frequency machine learning: Nowcasting and backcasting weekly initial claims with daily internet search volume data","volume":"39","author":"Borup","year":"2023","journal-title":"Int. J. Forecast."},{"key":"10.1016\/j.ins.2026.123881_b0175","doi-asserted-by":"crossref","DOI":"10.1016\/j.ins.2023.119165","article-title":"A novel data-driven seasonal multivariable grey model for seasonal time series forecasting","volume":"642","author":"Li","year":"2023","journal-title":"Inf. Sci."},{"key":"10.1016\/j.ins.2026.123881_b0180","doi-asserted-by":"crossref","first-page":"4776","DOI":"10.1016\/j.egyr.2022.03.166","article-title":"Application of a novel time-delay grey model based on mixed-frequency data to forecast the energy consumption in China","volume":"8","author":"Wan","year":"2022","journal-title":"Energy Rep."},{"key":"10.1016\/j.ins.2026.123881_b0185","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2024.123531","article-title":"Mixed-frequency data Sampling Grey system Model: forecasting annual CO2 emissions in China with quarterly and monthly economic-energy indicators","volume":"370","author":"An","year":"2024","journal-title":"Appl. Energy"},{"key":"10.1016\/j.ins.2026.123881_b0190","doi-asserted-by":"crossref","DOI":"10.1016\/j.renene.2025.123055","article-title":"Mixed-frequency fusion grey panel model for spatiotemporal prediction of photovoltaic power generation","volume":"248","author":"Zuo","year":"2025","journal-title":"Renew. Energy"},{"key":"10.1016\/j.ins.2026.123881_b0195","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2025.135442","article-title":"Mixed-frequency grey prediction model with fractional lags for electricity demand and estimation of coal power phase-out scale","volume":"320","author":"Gou","year":"2025","journal-title":"Energy"},{"key":"10.1016\/j.ins.2026.123881_b0200","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.econmod.2020.06.003","article-title":"Forecasting the Consumer Confidence Index with tree-based MIDAS regressions","volume":"91","author":"Qiu","year":"2020","journal-title":"Econ. Model."},{"key":"10.1016\/j.ins.2026.123881_b0205","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.118104","article-title":"A novel structure adaptive fractional discrete grey forecasting model and its application in China\u2019s crude oil production prediction","volume":"207","author":"Wang","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.ins.2026.123881_b0210","doi-asserted-by":"crossref","DOI":"10.1016\/j.irfa.2021.101854","article-title":"Modelling multiperiod patterns in stock-market reactions to events, with an application to serial acquisitions","volume":"77","author":"Doan","year":"2021","journal-title":"Int. Rev. Financ. Anal."},{"key":"10.1016\/j.ins.2026.123881_b0215","doi-asserted-by":"crossref","first-page":"819","DOI":"10.3390\/diagnostics16050819","article-title":"Optimization-driven hybrid machine learning framework for brain tumor classification in MRI with metaheuristic feature selection","volume":"16","author":"\u00d6zkan","year":"2026","journal-title":"Diagnostics"},{"key":"10.1016\/j.ins.2026.123881_b0220","doi-asserted-by":"crossref","DOI":"10.1016\/j.jclepro.2023.138401","article-title":"Prediction of combustion, performance, and emission parameters of ethanol powered spark ignition engine using ensemble Least Squares boosting machine learning algorithms","volume":"421","author":"Godwin","year":"2023","journal-title":"J. Clean. Prod."},{"key":"10.1016\/j.ins.2026.123881_b0225","doi-asserted-by":"crossref","unstructured":"Y. B. \u00d6z\u00e7elik, A. Altan, C. Kaya, MLapproach for early diagnosis of Alzheimer's disease using rs-fMRI and metaheuristic optimization with functional connectivity matrices, 2024 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET), Kota Kinabalu, Malaysia, 2024, pp. 248-253.","DOI":"10.1109\/IICAIET62352.2024.10730420"},{"key":"10.1016\/j.ins.2026.123881_b0230","doi-asserted-by":"crossref","DOI":"10.1016\/j.resconrec.2020.104781","article-title":"Life cycle assessment of emerging technologies on value recovery from hard disk drives","volume":"157","author":"Jin","year":"2020","journal-title":"Resour. Conserv. Recycl."},{"issue":"15","key":"10.1016\/j.ins.2026.123881_b0235","doi-asserted-by":"crossref","first-page":"14335","DOI":"10.1007\/s11071-023-08553-0","article-title":"Hopf bifurcation in a fractional-order neural network with self-connection delay","volume":"111","author":"Huang","year":"2023","journal-title":"Nonlinear Dyn."},{"key":"10.1016\/j.ins.2026.123881_b0240","doi-asserted-by":"crossref","unstructured":"E. O. Taga, M. E. Ildiz, S. Oymak, et al., TimePFN: Effective Multivariate Time Series Forecasting with Synthetic Data, arXiv:2502.16294, 2025.","DOI":"10.1609\/aaai.v39i19.34288"},{"key":"10.1016\/j.ins.2026.123881_b0245","doi-asserted-by":"crossref","DOI":"10.1016\/j.ins.2026.123167","article-title":"Risks analysis and countermeasures research of merchant fishing vessels collision accidents based on LLM and GRAA","volume":"739","author":"Wang","year":"2026","journal-title":"Inf. Sci."},{"key":"10.1016\/j.ins.2026.123881_b0250","series-title":"Grey Prediction and Decision Methods","first-page":"272","author":"Xiao","year":"2013"}],"container-title":["Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0020025526008121?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0020025526008121?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T13:55:25Z","timestamp":1783950925000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0020025526008121"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,11]]},"references-count":50,"alternative-id":["S0020025526008121"],"URL":"https:\/\/doi.org\/10.1016\/j.ins.2026.123881","relation":{},"ISSN":["0020-0255"],"issn-type":[{"value":"0020-0255","type":"print"}],"subject":[],"published":{"date-parts":[[2026,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Considering mixed-frequency data and multiple interactions for hard disk drive failure prediction: An integrated grey system framework","name":"articletitle","label":"Article Title"},{"value":"Information Sciences","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ins.2026.123881","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"123881"}}