{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T23:26:39Z","timestamp":1780356399799,"version":"3.54.1"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2025,4,7]],"date-time":"2025-04-07T00:00:00Z","timestamp":1743984000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,4,7]],"date-time":"2025-04-07T00:00:00Z","timestamp":1743984000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2022YFB3706902"],"award-info":[{"award-number":["2022YFB3706902"]}]},{"name":"innovation project for graduate students of Central South University","award":["1053320213586"],"award-info":[{"award-number":["1053320213586"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2025,5]]},"DOI":"10.1007\/s10489-025-06523-0","type":"journal-article","created":{"date-parts":[[2025,4,6]],"date-time":"2025-04-06T20:32:51Z","timestamp":1743971571000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["A novel hybrid statistical and neural network model for forecasting multivariate time series parameters in forging process"],"prefix":"10.1007","volume":"55","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-2563-6308","authenticated-orcid":false,"given":"Ning-Fu","family":"Zeng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9033-1564","authenticated-orcid":false,"given":"Yong-Cheng","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-1104-3829","authenticated-orcid":false,"given":"Miao","family":"Wan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gui-Cheng","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ming-Song","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,4,7]]},"reference":[{"key":"6523_CR1","doi-asserted-by":"publisher","first-page":"143260","DOI":"10.1016\/j.msea.2022.143260","volume":"845","author":"QY Zhao","year":"2022","unstructured":"Zhao QY, Sun QY, Xin SW, Chen YN, Wu C, Wang H et al (2022) High-strength titanium alloys for aerospace engineering applications: a review on melting-forging process. Mater Sci Eng A 845:143260. https:\/\/doi.org\/10.1016\/j.msea.2022.143260","journal-title":"Mater Sci Eng A"},{"issue":"1","key":"6523_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10845-023-02265-3","volume":"35","author":"A Kusiak","year":"2024","unstructured":"Kusiak A (2024) Hyper-automation in manufacturing industry. J Intell Manuf 35(1):1\u20132. https:\/\/doi.org\/10.1007\/s10845-023-02265-3","journal-title":"J Intell Manuf"},{"issue":"1","key":"6523_CR3","doi-asserted-by":"publisher","first-page":"22374","DOI":"10.1038\/s41598-023-49727-5","volume":"13","author":"M Todescato","year":"2023","unstructured":"Todescato M, Braholli O, Chaltsev D, Di Blasio I, Don D, Egger G et al (2023) Sustainable manufacturing through application of reconfigurable and intelligent systems in production processes: a system perspective. Sci Rep 13(1):22374. https:\/\/doi.org\/10.1038\/s41598-023-49727-5","journal-title":"Sci Rep"},{"issue":"5","key":"6523_CR4","doi-asserted-by":"publisher","first-page":"2200529","DOI":"10.1002\/srin.202200529","volume":"94","author":"S Sharma","year":"2023","unstructured":"Sharma S, Sharma M, Gupta V, Singh J (2023) A systematic review of factors affecting the process parameters and various measurement techniques in the forging processes. Steel Res Int 94(5):2200529. https:\/\/doi.org\/10.1002\/srin.202200529","journal-title":"Steel Res Int"},{"key":"6523_CR5","doi-asserted-by":"publisher","first-page":"109457","DOI":"10.1016\/j.ymssp.2022.109457","volume":"180","author":"IJ Jang","year":"2022","unstructured":"Jang IJ, Bae GY, Kim HS (2022) Metal forming defect detection method based on recurrence quantification analysis of time-series load signal measured by real-time monitoring system with bolt-type piezoelectric sensor. Mech Syst Signal Proc 180:109457. https:\/\/doi.org\/10.1016\/j.ymssp.2022.109457","journal-title":"Mech Syst Signal Proc"},{"key":"6523_CR6","doi-asserted-by":"publisher","first-page":"218","DOI":"10.28991\/esj-2024-08-01-016","volume":"8","author":"W Wydyanto","year":"2024","unstructured":"Wydyanto W, Nayan NM, Sulaiman R et al (2024) A hybrid approach to detect and identify text in picture. Emerg Sci J 8:218\u2013238. https:\/\/doi.org\/10.28991\/esj-2024-08-01-016","journal-title":"Emerg Sci J"},{"key":"6523_CR7","doi-asserted-by":"publisher","first-page":"200","DOI":"10.28991\/hij-2024-05-01-015","volume":"5","author":"J Hu","year":"2024","unstructured":"Hu J, Yan Y, Xie Z (2024) Automatic recognition technology of library books based on convolutional neural network model. HighTech Innov J 5:200\u2013212. https:\/\/doi.org\/10.28991\/hij-2024-05-01-015","journal-title":"HighTech Innov J"},{"key":"6523_CR8","doi-asserted-by":"publisher","first-page":"8009","DOI":"10.1007\/s10973-022-11827-1","volume":"148","author":"H Azimy","year":"2023","unstructured":"Azimy H, Azimy N, Meghdadi Isfahani AH et al (2023) Analysis of thermal performance and ultrasonic wave power variation on heat transfer of heat exchanger in the presence of nanofluid using the artificial neural network: experimental study and model fitting. J Therm Anal Calorim 148:8009\u20138023. https:\/\/doi.org\/10.1007\/s10973-022-11827-1","journal-title":"J Therm Anal Calorim"},{"key":"6523_CR9","doi-asserted-by":"publisher","first-page":"e17539","DOI":"10.1016\/j.heliyon.2023.e17539","volume":"9","author":"MH Razavi Dehkordi","year":"2023","unstructured":"Razavi Dehkordi MH, Alizadeh A, Zekri H et al (2023) Experimental study of thermal conductivity coefficient of GNSs-WO3\/LP107160 hybrid nanofluid and development of a practical ANN modeling for estimating thermal conductivity. Heliyon 9:e17539. https:\/\/doi.org\/10.1016\/j.heliyon.2023.e17539","journal-title":"Heliyon"},{"key":"6523_CR10","doi-asserted-by":"publisher","first-page":"109407","DOI":"10.1016\/j.optlastec.2023.109407","volume":"163","author":"C Sun","year":"2023","unstructured":"Sun C, Dehkordi MHR, Kholoud MJ et al (2023) Systematic evaluation of pulsed laser parameters effect on temperature distribution in dissimilar laser welding: a numerical simulation and artificial neural network. Opt Laser Technol 163:109407. https:\/\/doi.org\/10.1016\/j.optlastec.2023.109407","journal-title":"Opt Laser Technol"},{"key":"6523_CR11","doi-asserted-by":"publisher","first-page":"103364","DOI":"10.1016\/j.infrared.2020.103364","volume":"108","author":"Y Yongbin","year":"2020","unstructured":"Yongbin Y, Bagherzadeh SA, Azimy H et al (2020) Comparison of the artificial neural network model prediction and the experimental results for cutting region temperature and surface roughness in laser cutting of AL6061T6 alloy. Infrared Phys Technol 108:103364. https:\/\/doi.org\/10.1016\/j.infrared.2020.103364","journal-title":"Infrared Phys Technol"},{"key":"6523_CR12","doi-asserted-by":"publisher","first-page":"592","DOI":"10.28991\/esj-2024-08-02-014","volume":"8","author":"KW Goh","year":"2024","unstructured":"Goh KW, Surono S, Afiatin MYF et al (2024) Comparison of activation functions in convolutional neural network for poisson noisy image classification. Emerg Sci J 8:592\u2013602. https:\/\/doi.org\/10.28991\/esj-2024-08-02-014","journal-title":"Emerg Sci J"},{"issue":"6","key":"6523_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3533382","volume":"55","author":"K Benidis","year":"2023","unstructured":"Benidis K, Rangapuram SS, Flunkert V, Wang Y, Maddix D, Turkmen C et al (2023) Deep learning for time series forecasting: Tutorial and literature survey. ACM Comput Surv 55(6):1\u201336. https:\/\/doi.org\/10.1145\/3533382","journal-title":"ACM Comput Surv"},{"issue":"1","key":"6523_CR14","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.ijforecast.2019.05.011","volume":"36","author":"S Makridakis","year":"2020","unstructured":"Makridakis S, Hyndman RJ, Petropoulos F (2020) Forecasting in social settings: the state of the art. Int J Forecast 36(1):15\u201328. https:\/\/doi.org\/10.1016\/j.ijforecast.2019.05.011","journal-title":"Int J Forecast"},{"issue":"6","key":"6523_CR15","doi-asserted-by":"publisher","first-page":"219","DOI":"10.3390\/systems12060219","volume":"12","author":"MN \u0130nce","year":"2024","unstructured":"\u0130nce MN, Ta\u015fdemir \u00c7 (2024) Forecasting retail sales for furniture and furnishing items through the employment of multiple linear regression and Holt-Winters models. Systems 12(6):219. https:\/\/doi.org\/10.3390\/systems12060219","journal-title":"Systems"},{"key":"6523_CR16","doi-asserted-by":"publisher","first-page":"109579","DOI":"10.1016\/j.ijepes.2023.109579","volume":"155","author":"M Yamasaki","year":"2024","unstructured":"Yamasaki M, Freire RZ, Seman LO, Stefenon SF, Mariani VC, dos Santos CL (2024) Optimized hybrid ensemble learning approaches applied to very short-term load forecasting. Int J Electr Power Energy Syst 155:109579. https:\/\/doi.org\/10.1016\/j.ijepes.2023.109579","journal-title":"Int J Electr Power Energy Syst"},{"issue":"1","key":"6523_CR17","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1089\/big.2020.0159","volume":"9","author":"JF Torres","year":"2021","unstructured":"Torres JF, Hadjout D, Sebaa A, Mart\u00ednez-\u00c1lvarez F, Troncoso A (2021) Deep learning for time series forecasting: a survey. Big Data 9(1):3\u201321. https:\/\/doi.org\/10.1089\/big.2020.0159","journal-title":"Big Data"},{"issue":"2","key":"6523_CR18","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1002\/for.3024","volume":"43","author":"P Wang","year":"2024","unstructured":"Wang P, Gurmani SH, Tao Z, Liu J, Chen H (2024) Interval time series forecasting: a systematic literature review. J Forecast 43(2):249\u2013285. https:\/\/doi.org\/10.1002\/for.3024","journal-title":"J Forecast"},{"issue":"1","key":"6523_CR19","doi-asserted-by":"publisher","first-page":"1689","DOI":"10.1038\/s41598-024-52240-y","volume":"14","author":"JH Hao","year":"2024","unstructured":"Hao JH, Liu FG (2024) Improving long-term multivariate time series forecasting with a seasonal-trend decomposition-based 2-dimensional temporal convolution dense network. Sci Rep 14(1):1689. https:\/\/doi.org\/10.1038\/s41598-024-52240-y","journal-title":"Sci Rep"},{"key":"6523_CR20","doi-asserted-by":"publisher","first-page":"115597","DOI":"10.1016\/j.apenergy.2020.115597","volume":"278","author":"Y Chen","year":"2020","unstructured":"Chen Y, Koch T, Zakiyeva N, Zhu BZ (2020) Modeling and forecasting the dynamics of the natural gas transmission network in Germany with the demand and supply balance constraint. Appl Energy 278:115597. https:\/\/doi.org\/10.1016\/j.apenergy.2020.115597","journal-title":"Appl Energy"},{"issue":"2","key":"6523_CR21","doi-asserted-by":"publisher","first-page":"1977","DOI":"10.1109\/tii.2022.3198670","volume":"19","author":"YT Yao","year":"2023","unstructured":"Yao YT, Yang MH, Wang JY, Xie M (2023) Multivariate time-series prediction in industrial processes via a deep hybrid network under data uncertainty. IEEE Trans Ind Inform 19(2):1977\u20131987. https:\/\/doi.org\/10.1109\/tii.2022.3198670","journal-title":"IEEE Trans Ind Inform"},{"issue":"7","key":"6523_CR22","doi-asserted-by":"publisher","first-page":"3429","DOI":"10.1007\/s12206-017-0631-y","volume":"31","author":"J Richter","year":"2017","unstructured":"Richter J, Blohm T, Stonis M, Behrens B (2017) Analysis of an aluminum forging process in completely enclosed dies considering the numerical prediction of thin flash generation in small gaps. J Mech Sci Technol 31(7):3429\u20133435. https:\/\/doi.org\/10.1007\/s12206-017-0631-y","journal-title":"J Mech Sci Technol"},{"key":"6523_CR23","doi-asserted-by":"publisher","first-page":"114031","DOI":"10.1016\/j.rser.2023.114031","volume":"189","author":"Y Eren","year":"2024","unstructured":"Eren Y, K\u00fc\u00e7\u00fckdemiral \u0130 (2024) A comprehensive review on deep learning approaches for short-term load forecasting. Renew Sustain Energy Rev 189:114031. https:\/\/doi.org\/10.1016\/j.rser.2023.114031","journal-title":"Renew Sustain Energy Rev"},{"issue":"3","key":"6523_CR24","doi-asserted-by":"publisher","first-page":"849","DOI":"10.1007\/s00521-014-1562-8","volume":"25","author":"A Azari","year":"2014","unstructured":"Azari A, Poursina M, Poursina D (2014) Radial forging force prediction through MR, ANN, and ANFIS models. Neural Comput Appl 25(3):849\u2013858. https:\/\/doi.org\/10.1007\/s00521-014-1562-8","journal-title":"Neural Comput Appl"},{"issue":"9","key":"6523_CR25","doi-asserted-by":"publisher","first-page":"585","DOI":"10.1007\/s00521-016-2556-5","volume":"29","author":"YC Lin","year":"2018","unstructured":"Lin YC, Chen DD, Chen MS, Chen XM, Li J (2018) A precise BP neural network-based online model predictive control strategy for die forging hydraulic press machine. Neural Comput Appl 29(9):585\u2013596. https:\/\/doi.org\/10.1007\/s00521-016-2556-5","journal-title":"Neural Comput Appl"},{"issue":"12","key":"6523_CR26","doi-asserted-by":"publisher","first-page":"8243","DOI":"10.1109\/tii.2021.3065718","volume":"17","author":"SMJ Jalali","year":"2021","unstructured":"Jalali SMJ, Ahmadian S, Khosravi A, Shafie KM, Nahavandi S, Catal\u00e3o PSJ (2021) A novel evolutionary-based deep convolutional neural network model for intelligent load forecasting. IEEE Trans Ind Inform 17(12):8243\u20138253. https:\/\/doi.org\/10.1109\/tii.2021.3065718","journal-title":"IEEE Trans Ind Inform"},{"key":"6523_CR27","doi-asserted-by":"publisher","first-page":"108333","DOI":"10.1016\/j.intermet.2024.108333","volume":"170","author":"S Zhang","year":"2024","unstructured":"Zhang S, Lin YC, He DG, Jiang YQ, Zhang HJ, Zeng NF et al (2024) Correlation between plastic deformation mechanism and texture evolution of a near \u03b2-Ti alloy deformed in \u03b2 region. Intermetallics 170:108333. https:\/\/doi.org\/10.1016\/j.intermet.2024.108333","journal-title":"Intermetallics"},{"issue":"7","key":"6523_CR28","doi-asserted-by":"publisher","first-page":"3259","DOI":"10.1007\/s10845-023-02202-4","volume":"35","author":"L Xi","year":"2024","unstructured":"Xi L, Wang W, Chen JY, Wu XF (2024) Appending-inspired multivariate time series association fusion for tool condition monitoring. J Intell Manuf 35(7):3259\u20133272. https:\/\/doi.org\/10.1007\/s10845-023-02202-4","journal-title":"J Intell Manuf"},{"issue":"6","key":"6523_CR29","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1007\/s10462-024-10759-6","volume":"57","author":"WX Chen","year":"2024","unstructured":"Chen WX, Yang KX, Yu ZW, Shi YF, Chen CLP (2024) A survey on imbalanced learning: latest research, applications and future directions. Artif Intell Rev 57(6):137. https:\/\/doi.org\/10.1007\/s10462-024-10759-6","journal-title":"Artif Intell Rev"},{"key":"6523_CR30","doi-asserted-by":"publisher","unstructured":"He KM, Zhang XY, Ren SQ, Sun J (2016) Deep residual learning for image recognition. In: 2016 IEEE Conference on CVPR. https:\/\/doi.org\/10.1109\/cvpr.2016.90","DOI":"10.1109\/cvpr.2016.90"},{"key":"6523_CR31","doi-asserted-by":"publisher","first-page":"884","DOI":"10.1016\/j.ijforecast.2022.03.001","volume":"39","author":"KG Olivares","year":"2023","unstructured":"Olivares KG, Challu C, Marcjasz G, Weron R, Dubrawski A (2023) Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx. Int J Forecast 39:884\u2013900. https:\/\/doi.org\/10.1016\/j.ijforecast.2022.03.001","journal-title":"Int J Forecast"},{"key":"6523_CR32","doi-asserted-by":"publisher","unstructured":"Mehrmolaei S, Keyvanpour MR (2016) Time series forecasting using improved ARIMA. In: 2016 Artificial Intelligence and Robotics. https:\/\/doi.org\/10.1109\/rios.2016.7529496","DOI":"10.1109\/rios.2016.7529496"},{"issue":"6","key":"6523_CR33","doi-asserted-by":"publisher","first-page":"704","DOI":"10.1007\/s42979-024-03060-4","volume":"5","author":"S Saleti","year":"2024","unstructured":"Saleti S, Panchumarthi LY, Kallam YR, Parchuri L, Jitte S (2024) Enhancing forecasting accuracy with a moving average-integrated hybrid ARIMA-LSTM model. SN Comput Sci 5(6):704. https:\/\/doi.org\/10.1007\/s42979-024-03060-4","journal-title":"SN Comput Sci"},{"key":"6523_CR34","doi-asserted-by":"crossref","unstructured":"Lea C, Flynn MD, Vidal R et al (2017) Temporal Convolutional Networks for Action Segmentation and Detection. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, HI, USA, pp 1003\u20131012","DOI":"10.1109\/CVPR.2017.113"},{"key":"6523_CR35","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1016\/j.procs.2021.08.015","volume":"192","author":"R Orhand","year":"2021","unstructured":"Orhand R, Khodji H, Hutt A, Jeannin-Girardon A (2021) Quantification of the transferability of features between deep neural networks. Procedia Comput Sci 192:138\u2013147. https:\/\/doi.org\/10.1016\/j.procs.2021.08.015","journal-title":"Procedia Comput Sci"},{"issue":"1","key":"6523_CR36","doi-asserted-by":"publisher","first-page":"23899","DOI":"10.1038\/s41598-021-03282-z","volume":"11","author":"RL Abduljabbar","year":"2021","unstructured":"Abduljabbar RL, Dia H, Tsai P (2021) Development and evaluation of bidirectional LSTM freeway traffic forecasting models using simulation data. Sci Rep 11(1):23899. https:\/\/doi.org\/10.1038\/s41598-021-03282-z","journal-title":"Sci Rep"},{"key":"6523_CR37","unstructured":"Nair V, Hinton GE (2010) Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning. https:\/\/dl.acm.org\/doi\/10.5555\/3104322.3104425. Accessed 21 June 2010"},{"key":"6523_CR38","doi-asserted-by":"publisher","first-page":"175293","DOI":"10.1016\/j.jallcom.2024.175293","volume":"1002","author":"GC Wu","year":"2024","unstructured":"Wu GC, Lin YC, Chen MS, Qiu W, Zeng NF, Zhang S et al (2024) Continuous dynamic recrystallization behaviors in a single-phase deformed Ti-55511 alloy by cellular automata model. J Alloy Compd 1002:175293. https:\/\/doi.org\/10.1016\/j.jallcom.2024.175293","journal-title":"J Alloy Compd"},{"key":"6523_CR39","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1016\/j.neucom.2022.03.054","volume":"491","author":"YT Li","year":"2022","unstructured":"Li YT, Li Y (2022) Predicting chaotic time series and replicating chaotic attractors based on two novel echo state network models. Neurocomputing 491:321\u2013332. https:\/\/doi.org\/10.1016\/j.neucom.2022.03.054","journal-title":"Neurocomputing"},{"key":"6523_CR40","doi-asserted-by":"publisher","unstructured":"Zhong ZJ, Yu ZW, Fan ZW, Chen CLP, Yang KX (2024) Adaptive memory broad learning system for unsupervised time series anomaly detection. IEEE Trans Neural Netw Learn Syst 1\u201315. https:\/\/doi.org\/10.1109\/tnnls.2024.3415621","DOI":"10.1109\/tnnls.2024.3415621"},{"issue":"10","key":"6523_CR41","doi-asserted-by":"publisher","first-page":"6631","DOI":"10.1109\/tii.2021.3112487","volume":"18","author":"JF Li","year":"2022","unstructured":"Li JF, Yang CJ, Li YX, Xie SJ (2022) A context-aware enhanced GRU network with feature-temporal attention for prediction of silicon content in hot metal. IEEE Trans Ind Inform 18(10):6631\u20136641. https:\/\/doi.org\/10.1109\/tii.2021.3112487","journal-title":"IEEE Trans Ind Inform"},{"issue":"22","key":"6523_CR42","doi-asserted-by":"publisher","first-page":"6750","DOI":"10.3390\/ma14226750","volume":"14","author":"G Su","year":"2021","unstructured":"Su G, Yun Z, Lin YC, He DG, Zhang S, Chen ZJ (2021) Microstructure evolution and a unified constitutive model of Ti-55511 alloy compressed at stepped strain rates. Materials 14(22):6750. https:\/\/doi.org\/10.3390\/ma14226750","journal-title":"Materials"},{"key":"6523_CR43","unstructured":"Xu K, Ba JL, Kiros R, Cho KH, Courville A, Salakhutdinov R et al (2015) Show, attend and tell: neural image caption generation with visual attention. In: Proceedings of the 32nd International Conference on International Conference on Machine Learning, (ICML). Lille, France, pp 2048\u20132057"},{"issue":"8","key":"6523_CR44","doi-asserted-by":"publisher","first-page":"1421","DOI":"10.1007\/s10994-019-05815-0","volume":"108","author":"S Shih","year":"2019","unstructured":"Shih S, Sun F, Lee H (2019) Temporal pattern attention for multivariate time series forecasting. Mach Learn 108(8):1421\u20131441. https:\/\/doi.org\/10.1007\/s10994-019-05815-0","journal-title":"Mach Learn"},{"issue":"3","key":"6523_CR45","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1007\/s00521-010-0407-3","volume":"20","author":"SG Da","year":"2011","unstructured":"Da SG, Ludermir TB, Lima LMR (2011) Comparison of new activation functions in neural network for forecasting financial time series. Neural Comput Appl 20(3):417\u2013439. https:\/\/doi.org\/10.1007\/s00521-010-0407-3","journal-title":"Neural Comput Appl"},{"issue":"11","key":"6523_CR46","doi-asserted-by":"publisher","first-page":"598","DOI":"10.3390\/info14110598","volume":"14","author":"A Casolaro","year":"2023","unstructured":"Casolaro A, Capone V, Iannuzzo G, Camastra F (2023) Deep learning for time series forecasting: advances and open problems. Information 14(11):598. https:\/\/doi.org\/10.3390\/info14110598","journal-title":"Information"},{"key":"6523_CR47","doi-asserted-by":"publisher","first-page":"101474","DOI":"10.1016\/j.seta.2021.101474","volume":"47","author":"A Kumar Dubey","year":"2021","unstructured":"Kumar Dubey A, Kumar A, Garc\u00eda-D\u00edaz V et al (2021) Study and analysis of SARIMA and LSTM in forecasting time series data. Sustain Energy Technol Assessments 47:101474. https:\/\/doi.org\/10.1016\/j.seta.2021.101474","journal-title":"Sustain Energy Technol Assessments"},{"issue":"8","key":"6523_CR48","doi-asserted-by":"publisher","first-page":"255","DOI":"10.3390\/fi15080255","volume":"15","author":"VI Kontopoulou","year":"2023","unstructured":"Kontopoulou VI, Panagopoulos AD, Kakkos I, Matsopoulos GK (2023) A review of ARIMA vs machine learning approaches for time series forecasting in data driven networks. Future Internet 15(8):255. https:\/\/doi.org\/10.3390\/fi15080255","journal-title":"Future Internet"},{"issue":"1","key":"6523_CR49","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1631\/fitee.2300310","volume":"25","author":"LQ Lin","year":"2024","unstructured":"Lin LQ, Li ZK, Li RK, Li XF, Gao JB (2024) Diffusion models for time-series applications: a survey. Front Inf Technol Elect Eng 25(1):19\u201341. https:\/\/doi.org\/10.1631\/fitee.2300310","journal-title":"Front Inf Technol Elect Eng"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-025-06523-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-025-06523-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-025-06523-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T19:34:46Z","timestamp":1758310486000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-025-06523-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,7]]},"references-count":49,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2025,5]]}},"alternative-id":["6523"],"URL":"https:\/\/doi.org\/10.1007\/s10489-025-06523-0","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,7]]},"assertion":[{"value":"26 March 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 April 2025","order":2,"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 known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interest"}}],"article-number":"630"}}