{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T23:04:20Z","timestamp":1772751860745,"version":"3.50.1"},"reference-count":58,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Guangdong Provincial Key Research Institute of Humanities and Social Sciences\/Center for Translation Studies at Guangdong University of Foreign Studies","award":["CTS202111"],"award-info":[{"award-number":["CTS202111"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>Accurate forecasting of language service demand is essential for language industry planning and resource allocation, yet it remains challenging due to small sample sizes, noisy data, and nonlinear dynamics in industry-level time series. To enhance forecasting accuracy, this study proposes a novel hybrid forecasting framework, called the Sine Cosine Algorithm-optimized wavelet analysis-based new information priority nonhomogeneous discrete grey model (SCA\u2013WA\u2013NIPNDGM). By integrating wavelet-based denoising with the NIPNDGM, the model effectively extracts intrinsic signals and prioritizes recent observations to capture short-term trends while addressing nonlinear parameter estimation via heuristic optimization. Empirical studies are conducted across three high-demand sectors in China from 2000 to 2024, including manufacturing; water conservancy, environmental, and public facilities management; and wholesale and retail. The findings show that the proposed model displays superior performance to 11 benchmark grey models and five optimization algorithms across six evaluation metrics, achieving test Mean Absolute Percentage Error (MAPE) values as low as 1.2%, with strong generalization, stable iterations, and fast convergence. These results underscore its effectiveness in forecasting complex time series and offer valuable insights for language service market planning under emerging AI-driven disruptions.<\/jats:p>","DOI":"10.3390\/systems13090768","type":"journal-article","created":{"date-parts":[[2025,9,2]],"date-time":"2025-09-02T14:16:55Z","timestamp":1756822615000},"page":"768","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Hybrid Wavelet Analysis-Based New Information Priority Nonhomogeneous Discrete Grey Model with SCA Optimization for Language Service Demand Forecasting"],"prefix":"10.3390","volume":"13","author":[{"given":"Xixi","family":"Li","sequence":"first","affiliation":[{"name":"College of Translation and Interpreting, Sichuan International Studies University, Chongqing 400031, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7847-911X","authenticated-orcid":false,"given":"Xin","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Mathematics and Physics, Southwest University of Science and Technology, Mianyang 621010, China"},{"name":"Center for Information Management and Service Studies of Sichuan, Southwest University of Science and Technology, Mianyang 621010, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,1]]},"reference":[{"key":"ref_1","first-page":"13","article-title":"Exploring Chinese Economic Discourse and Translation Strategies in the Era of AI: A Digital-Tech Approach","volume":"4","author":"Wang","year":"2024","journal-title":"Commun. Across Borders Transl. Interpret."},{"key":"ref_2","first-page":"2852","article-title":"A Frontier Exploration of Translation Industry Research in the Age of Artificial Intelligence","volume":"8","author":"Gao","year":"2024","journal-title":"J. Humanit. Arts Soc. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"106274","DOI":"10.1016\/j.resconrec.2022.106274","article-title":"Industry 4.0 in sustainable supply chain collaboration: Insights from an interview study with international buying firms and Chinese suppliers in the electronics industry","volume":"182","author":"Kunkel","year":"2022","journal-title":"Resour. Conserv. Recycl."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"100201","DOI":"10.1016\/j.iedeen.2022.100201","article-title":"The roles of competition on innovation efficiency and firm performance: Evidence from the Chinese manufacturing industry","volume":"29","author":"Huang","year":"2023","journal-title":"Eur. Res. Manag. Bus. Econ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"14935","DOI":"10.1007\/s13132-023-01673-3","article-title":"Enhancing competitiveness in cross-border e-commerce through knowledge-based consumer perception theory: An exploration of translation ability","volume":"15","author":"Tang","year":"2024","journal-title":"J. Knowl. Econ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"133395","DOI":"10.1016\/j.energy.2024.133395","article-title":"Energy management with adaptive moving average filter and deep deterministic policy gradient reinforcement learning for fuel cell hybrid electric vehicles","volume":"312","author":"Zhao","year":"2024","journal-title":"Energy"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"110113","DOI":"10.1016\/j.sigpro.2025.110113","article-title":"Tensor-based higher-order multivariate singular spectrum analysis and applications to multichannel biomedical signal analysis","volume":"238","author":"Le","year":"2026","journal-title":"Signal Process."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"103267","DOI":"10.1016\/j.aei.2025.103267","article-title":"A new perspective on non-ferrous metal price forecasting: An interpretable two-stage ensemble learning-based interval-valued forecasting system","volume":"65","author":"Yang","year":"2025","journal-title":"Adv. Eng. Inform."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"130643","DOI":"10.1016\/j.physa.2025.130643","article-title":"Unveiling the interdependency of cryptocurrency and Indian stocks through wavelet and nonlinear time series analysis: An Econophysics approach","volume":"670","author":"Moni","year":"2025","journal-title":"Phys. Stat. Mech. Its Appl."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1016\/S0167-6911(82)80025-X","article-title":"Control problems of grey systems","volume":"1","author":"Deng","year":"1982","journal-title":"Syst. Control Lett."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"135447","DOI":"10.1016\/j.scitotenv.2019.135447","article-title":"A novel conformable fractional non-homogeneous grey model for forecasting carbon dioxide emissions of BRICS countries","volume":"707","author":"Wu","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"498","DOI":"10.1108\/GS-02-2020-0023","article-title":"Research and application of novel Euler polynomial-driven grey model for short-term PM10 forecasting","volume":"11","author":"Xiang","year":"2021","journal-title":"Grey Syst. Theory Appl."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"105665","DOI":"10.1016\/j.cnsns.2020.105665","article-title":"The damping accumulated grey model and its application","volume":"95","author":"Liu","year":"2021","journal-title":"Commun. Nonlinear Sci. Numer. Simul."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"108800","DOI":"10.1016\/j.epsr.2022.108800","article-title":"Optimized Fractional Overhead Power Term Polynomial Grey Model (OFOPGM) for market clearing price prediction","volume":"214","author":"Saxena","year":"2023","journal-title":"Electr. Power Syst. Res."},{"key":"ref_15","unstructured":"Xie, N., and Liu, S. (2005, January 12). Research on Discrete Grey Model and Its Mechanism. Proceedings of the 2005 IEEE International Conference on Systems, Man and Cybernetics, Waikoloa, HI, USA."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1109\/JSEE.2015.00013","article-title":"Interval grey number sequence prediction by using non-homogenous exponential discrete grey forecasting model","volume":"26","author":"Xie","year":"2015","journal-title":"J. Syst. Eng. Electron."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1108\/K-05-2017-0159","article-title":"Measurement of shock effect following change of one-child policy based on grey forecasting approach","volume":"47","author":"Xie","year":"2018","journal-title":"Kybernetes"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1932","DOI":"10.1016\/j.apm.2020.08.080","article-title":"Forecasting the renewable energy consumption of the European countries by an adjacent non-homogeneous grey model","volume":"89","author":"Liu","year":"2021","journal-title":"Appl. Math. Model."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"105493","DOI":"10.1016\/j.cnsns.2020.105493","article-title":"A novel discrete grey seasonal model and its applications","volume":"93","author":"Zhou","year":"2021","journal-title":"Commun. Nonlinear Sci. Numer. Simul."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"113644","DOI":"10.1016\/j.enconman.2020.113644","article-title":"A novel adaptive discrete grey model with time-varying parameters for long-term photovoltaic power generation forecasting","volume":"227","author":"Ding","year":"2021","journal-title":"Energy Convers. Manag."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"115761","DOI":"10.1016\/j.eswa.2021.115761","article-title":"A novel structural adaptive discrete grey prediction model and its application in forecasting renewable energy generation","volume":"186","author":"Qian","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3213","DOI":"10.1007\/s00500-022-07523-9","article-title":"Application of the three-parameter discrete direct grey model to forecast Chinas natural gas consumption","volume":"27","author":"Zhou","year":"2023","journal-title":"Soft Comput."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1775","DOI":"10.1016\/j.cnsns.2012.11.017","article-title":"Grey system model with the fractional order accumulation","volume":"18","author":"Wu","year":"2013","journal-title":"Commun. Nonlinear Sci. Numer. Simul."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1215","DOI":"10.1007\/s00521-014-1605-1","article-title":"Non-homogenous discrete grey model with fractional-order accumulation","volume":"25","author":"Wu","year":"2014","journal-title":"Neural Comput. Appl."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"117868","DOI":"10.1016\/j.enconman.2023.117868","article-title":"A new perspective of wind speed forecasting: Multi-objective and model selection-based ensemble interval-valued wind speed forecasting system","volume":"299","author":"Hao","year":"2024","journal-title":"Energy Convers. Manag."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"109915","DOI":"10.1016\/j.chaos.2020.109915","article-title":"Fractional Hausdorff grey model and its properties","volume":"138","author":"Chen","year":"2020","journal-title":"Chaos Solitons Fractals"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1016\/j.apm.2022.06.042","article-title":"Weakened fractional-order accumulation operator for ill-conditioned discrete grey system models","volume":"111","author":"Zhu","year":"2022","journal-title":"Appl. Math. Model."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"110942","DOI":"10.1016\/j.engappai.2025.110942","article-title":"Time-delayed fractional grey Bernoulli model with independent fractional orders for fossil energy consumption forecasting","volume":"155","author":"Ma","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1007\/s40314-024-03013-w","article-title":"A novel fractional Bessel grey system model optimized by Salp Swarm Algorithm for renewable energy generation forecasting in developed countries of Europe and North America","volume":"44","author":"Ma","year":"2025","journal-title":"Comput. Appl. Math."},{"key":"ref_30","first-page":"140","article-title":"New information priority accumulated grey discrete model and its application","volume":"25","author":"Zhou","year":"2017","journal-title":"Chin. J. Manag. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"122052","DOI":"10.1016\/j.renene.2024.122052","article-title":"A novel structure adaptive new information priority grey Bernoulli model and its application in China\u2019s renewable energy production","volume":"239","author":"Wang","year":"2025","journal-title":"Renew. Energy"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"124199","DOI":"10.1016\/j.eswa.2024.124199","article-title":"A new information priority grey prediction model for forecasting wind electricity generation with targeted regional hierarchy","volume":"252","author":"Guo","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"119854","DOI":"10.1016\/j.apenergy.2022.119854","article-title":"A novel structure adaptive new information priority discrete grey prediction model and its application in renewable energy generation forecasting","volume":"325","author":"He","year":"2022","journal-title":"Appl. Energy"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"150859","DOI":"10.1016\/j.scitotenv.2021.150859","article-title":"Forecasting greenhouse gas emissions with the new information priority generalized accumulative grey model","volume":"807","author":"Li","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"113476","DOI":"10.1016\/j.ijsolstr.2025.113476","article-title":"A multi-objective gradient-based approach for prestress and size optimization of cable domes","volume":"320","author":"Pollini","year":"2025","journal-title":"Int. J. Solids Struct."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"115312","DOI":"10.1016\/j.tcs.2025.115312","article-title":"Efficient deterministic algorithms for maximizing symmetric submodular functions","volume":"1046","author":"Wan","year":"2025","journal-title":"Theor. Comput. Sci."},{"key":"ref_37","first-page":"2869","article-title":"An Adaptive Firefly Algorithm for Dependent Task Scheduling in IoT-Fog Computing","volume":"142","author":"Yousif","year":"2025","journal-title":"Cmes Comput. Model. Eng. Sci."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"104774","DOI":"10.1016\/j.rineng.2025.104774","article-title":"Enhanced optimisation of MPLS network traffic using a novel adjustable Bat algorithm with loudness optimizer","volume":"26","author":"Masood","year":"2025","journal-title":"Results Eng."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Sowmiya, M., Banu Rekha, B., and Malar, E. (2025). Optimized heart disease prediction model using a meta-heuristic feature selection with improved binary salp swarm algorithm and stacking classifier. Comput. Biol. Med., 191.","DOI":"10.1016\/j.compbiomed.2025.110171"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"127578","DOI":"10.1016\/j.eswa.2025.127578","article-title":"AEPSO: An adaptive learning particle swarm optimization for solving the hyperparameters of dynamic periodic regulation grey model","volume":"283","author":"Hu","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"113436","DOI":"10.1016\/j.asoc.2025.113436","article-title":"A self-learning whale optimization algorithm based on reinforcement learning for a dual-resource flexible job shop scheduling problem","volume":"180","author":"Manafi","year":"2025","journal-title":"Appl. Soft Comput."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"103703","DOI":"10.1016\/j.rineng.2024.103703","article-title":"The quick crisscross sine cosine algorithm for optimal FACTS placement in uncertain wind integrated scenario based power systems","volume":"25","author":"Agrawal","year":"2025","journal-title":"Results Eng."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1016\/j.apm.2024.06.015","article-title":"The nonlinear multi-variable grey Bernoulli model and its applications","volume":"134","author":"He","year":"2024","journal-title":"Appl. Math. Model."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1237","DOI":"10.21105\/joss.01237","article-title":"PyWavelets: A Python package for wavelet analysis","volume":"4","author":"Lee","year":"2019","journal-title":"J. Open Source Softw."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"674","DOI":"10.1109\/34.192463","article-title":"A theory for multiresolution signal decomposition: The wavelet representation","volume":"11","author":"Mallat","year":"1989","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"613","DOI":"10.1109\/18.382009","article-title":"De-noising by soft-thresholding","volume":"41","author":"Donoho","year":"2002","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1173","DOI":"10.1016\/j.apm.2008.01.011","article-title":"Discrete grey forecasting model and its optimization","volume":"33","author":"Xie","year":"2009","journal-title":"Appl. Math. Model."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.knosys.2015.12.022","article-title":"SCA: A Sine Cosine Algorithm for solving optimization problems","volume":"96","author":"Mirjalili","year":"2016","journal-title":"Knowl.-Based Syst."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"3869619","DOI":"10.1155\/2018\/3869619","article-title":"Forecasting crude oil consumption in China using a grey prediction model with an optimal fractional-order accumulating operator","volume":"2018","author":"Duan","year":"2018","journal-title":"Complexity"},{"key":"ref_50","first-page":"686501","article-title":"Using a novel grey system model to forecast natural gas consumption in China","volume":"2015","author":"Wu","year":"2015","journal-title":"Math. Probl. Eng."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1194","DOI":"10.1016\/j.cnsns.2006.08.008","article-title":"Forecasting of foreign exchange rates of Taiwan\u2019s major trading partners by novel nonlinear Grey Bernoulli model NGBM (1, 1)","volume":"13","author":"Chen","year":"2008","journal-title":"Commun. Nonlinear Sci. Numer. Simul."},{"key":"ref_52","first-page":"1702","article-title":"Novel grey forecasting model and its modeling mechanism","volume":"24","author":"Cui","year":"2009","journal-title":"Control Decis."},{"key":"ref_53","unstructured":"Yang, X.S. (2010). Nature-Inspired Metaheuristic Algorithms, Luniver Press."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Gonz\u00e1lez, J.R., Pelta, D.A., Cruz, C., Terrazas, G., and Krasnogor, N. (2010). A New Metaheuristic Bat-Inspired Algorithm. Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), Springer.","DOI":"10.1007\/978-3-642-12538-6"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.advengsoft.2017.07.002","article-title":"Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems","volume":"114","author":"Mirjalili","year":"2017","journal-title":"Adv. Eng. Softw."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"108488","DOI":"10.1016\/j.agrformet.2021.108488","article-title":"Estimation of actual evapotranspiration and its components in an irrigated area by integrating the Shuttleworth-Wallace and surface temperature-vegetation index schemes using the particle swarm optimization algorithm","volume":"307","author":"Cui","year":"2021","journal-title":"Agric. For. Meteorol."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.advengsoft.2016.01.008","article-title":"The Whale Optimization Algorithm","volume":"95","author":"Mirjalili","year":"2016","journal-title":"Adv. Eng. Softw."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Siu, S.C. (2023). ChatGPT and GPT-4 for professional translators: Exploring the potential of large language models in translation. SSRN Electron. J.","DOI":"10.2139\/ssrn.4448091"}],"container-title":["Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-8954\/13\/9\/768\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:37:20Z","timestamp":1760035040000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-8954\/13\/9\/768"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,1]]},"references-count":58,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["systems13090768"],"URL":"https:\/\/doi.org\/10.3390\/systems13090768","relation":{},"ISSN":["2079-8954"],"issn-type":[{"value":"2079-8954","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,1]]}}}