{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,29]],"date-time":"2026-03-29T18:00:46Z","timestamp":1774807246216,"version":"3.50.1"},"reference-count":150,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,10,23]],"date-time":"2024-10-23T00:00:00Z","timestamp":1729641600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>This study aims to introduce a novel approach for predicting China\u2019s consumer confidence index (CCI), a key economic indicator that reflects consumers\u2019 confidence in current and future economic conditions. While traditional statistical models and economic indicators are the primary tools for forecasting CCI, their reliance on linear assumptions limits their ability to capture the complex, dynamic relationships inherent in economic systems. In response, this study proposes a two-step method that integrates social network analysis (SNA) and machine learning (ML) to enhance prediction accuracy by accounting for the nonlinear interactions and systemic interdependencies that drive consumer confidence. The use of SNA enables the identification of critical variables and their interconnected roles in shaping consumer sentiment, while ML models, specifically the gradient boosting decision tree (GBDT), leverage these relationships to provide more precise predictions. Utilizing monthly data from 1999 to 2023, the combined SNA and GBDT approach significantly improves the accuracy of CCI forecasts, particularly during periods of high volatility. The results of this study hold substantial value for policymakers, market analysts, and economists, as they offer a systems-oriented framework for economic forecasting. By demonstrating the effectiveness of combining SNA with ML technologies, this research not only advances the methodological toolkit for economic forecasting, but also provides a new lens through which the complex, adaptive nature of economic systems can be better understood and managed. This integrated approach paves the way for future developments in forecasting models that more accurately reflect the evolving dynamics of consumer confidence in a rapidly changing economic environment.<\/jats:p>","DOI":"10.3390\/systems12110445","type":"journal-article","created":{"date-parts":[[2024,10,23]],"date-time":"2024-10-23T04:28:43Z","timestamp":1729657723000},"page":"445","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Integrated Systematic Framework for Forecasting China\u2019s Consumer Confidence: A Machine Learning Approach"],"prefix":"10.3390","volume":"12","author":[{"given":"Yu-Cheng","family":"Lin","sequence":"first","affiliation":[{"name":"Department of International Commerce and Business, Graduate School of Konkuk University, Seoul 05029, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-3584-4144","authenticated-orcid":false,"given":"Bongsuk","family":"Sung","sequence":"additional","affiliation":[{"name":"Department of International Trade, Kyonggi University, Suwon-si 15442, Gyeonggi-do, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6366-8532","authenticated-orcid":false,"given":"Sang-Do","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Global Business, Konkuk University, Seoul 05029, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Bui, Y., and Hwang, H. (2024). Market sentiment and SPACs. Appl. Econ. Lett., 1\u20136.","DOI":"10.1080\/13504851.2023.2301479"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"129","DOI":"10.22219\/jibe.v7i02.28227","article-title":"Tweeting the economy: Analyzing social media sentiments and macroeconomic indicators","volume":"7","author":"Fitriani","year":"2023","journal-title":"J. Innov. Bus. Econ."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Jiang, C., Gao, H., and Xu, Q. (2024). China\u2019s risk contagion using the mixed-frequency macro-financial network. Econ. Syst., 101212.","DOI":"10.1016\/j.ecosys.2024.101212"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"107423","DOI":"10.1016\/j.eneco.2024.107423","article-title":"Unveiling the enigma: Exploring how uncertain crude oil prices shape investment expenditure and efficiency in Chinese enterprises","volume":"132","author":"Shang","year":"2024","journal-title":"Energy Econ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.worlddev.2015.11.001","article-title":"Constructing a Ladder for Growth: Policy, Markets, and Industrial Upgrading in China","volume":"80","author":"Brandt","year":"2016","journal-title":"World Dev."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Chen, Y. (2024). Research on the impact of the digital economy on the level of industrial structure: An empirical study of 280 cities in China. PLoS ONE, 19.","DOI":"10.1371\/journal.pone.0298343"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3010","DOI":"10.2166\/wcc.2023.051","article-title":"Carbon constraints, industrial structure upgrading, and green total factor productivity: An empirical study based on the Yangtze River Economic Belt","volume":"14","author":"You","year":"2023","journal-title":"J. Water Clim. Chang."},{"key":"ref_8","first-page":"17","article-title":"Consumer Confidence Index and Economic Growth: An Empirical Analysis of EU Countries","volume":"35","author":"Islam","year":"2016","journal-title":"EuroEconomica"},{"key":"ref_9","unstructured":"Luo, C., Li, Y., and Dong, L. (2024, September 09). The Evolution of Financial Risk Contagion During the COVID-19 Crisis Period: An Analysis Based on the Multiscale Complex Networks. Available online: https:\/\/ssrn.com\/abstract=4049423."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Sun, W., Chen, H., Liu, F., and Wang, Y. (2022). Point and interval prediction of crude oil futures prices based on chaos theory and multiobjective slime mold algorithm. Ann. Oper. Res., 1\u201331.","DOI":"10.1007\/s10479-022-04781-6"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Wang, D., and Fang, T. (2022). Forecasting Crude Oil Prices with a WT-FNN Model. Energies, 15.","DOI":"10.3390\/en15061955"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"105080","DOI":"10.1016\/j.eneco.2020.105080","article-title":"Neural network prediction of crude oil futures using B-splines","volume":"94","author":"Butler","year":"2021","journal-title":"Energy Econ."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Kalayc\u0131, B., Purut\u00e7uo\u011flu, V., and Weber, G.W. (2024). Optimal model description of finance and human factor indices. Cent. Eur. J. Oper. Res.","DOI":"10.1007\/s10100-023-00897-7"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"105936","DOI":"10.1016\/j.eneco.2022.105936","article-title":"Forecasting crude oil volatility with uncertainty indicators: New evidence","volume":"108","author":"Li","year":"2022","journal-title":"Energy Econ."},{"key":"ref_15","unstructured":"Mohanty, S.N., Diaz, V.G., and Kumar, G.S. (2022, January 16\u201317). Intelligent Systems and Machine Learning. Proceedings of the First EAI International Conference, ICISML 2022, Hyderabad, India. Proceedings, Part I."},{"key":"ref_16","unstructured":"Elsaied, M. (2023). Developing and Forecasting the Egyptian Construction Cost Index. [Master\u2019s Thesis, The American University in Cairo]."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ljungberg, J. (2024). European Consumer Price Indices Since 1870, Lund University.","DOI":"10.1007\/s11698-024-00283-6"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Sopgoui, L. (2024). Modeling the impact of Climate transition on real estate prices. arXiv.","DOI":"10.2139\/ssrn.4913777"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1016\/j.eneco.2018.10.026","article-title":"Forecasting oil prices: High-frequency financial data are indeed useful","volume":"76","author":"Degiannakis","year":"2018","journal-title":"Energy Econ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1016\/j.eneco.2016.03.017","article-title":"Predicting the oil prices: Do technical indicators help?","volume":"56","author":"Yin","year":"2016","journal-title":"Energy Econ."},{"key":"ref_21","first-page":"878","article-title":"Examining the Impact of COVID-19 and Economic Indicators on US GDP using Midas-Simulation and Empirical Evidence","volume":"21","author":"Safi","year":"2024","journal-title":"Migrat. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1061\/(ASCE)CO.1943-7862.0000006","article-title":"Dynamic Regression Models for Prediction of Construction Costs","volume":"135","author":"Hwang","year":"2009","journal-title":"J. Constr. Eng. Manag."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"656","DOI":"10.1061\/(ASCE)CO.1943-7862.0000350","article-title":"Time Series Models for Forecasting Construction Costs Using Time Series Indexes","volume":"137","author":"Hwang","year":"2011","journal-title":"J. Constr. Eng. Manag."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Caiazzo, F., and Caggiano, A. (2018). Laser Direct Metal Deposition of 2024 Al Alloy: Trace Geometry Prediction via Machine Learning. Materials, 11.","DOI":"10.3390\/ma11030444"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2841","DOI":"10.1007\/s13369-022-07009-8","article-title":"Prediction of Mechanical Properties of the 2024 Aluminum Alloy by Using Machine Learning Methods","volume":"48","year":"2023","journal-title":"Arab. J. Sci. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1007\/s10902-022-00603-5","article-title":"The Effect of Consumer Confidence and Subjective Well-being on Consumers\u2019 Spending Behavior","volume":"24","year":"2023","journal-title":"J. Happiness Stud."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ahmad, N., and Rangaraju, S.K. (2017). Impact of Consumer Confidence During Good Times and Bad Times, Social Science Electronic Publishing.","DOI":"10.2139\/ssrn.2939350"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2136","DOI":"10.1080\/1540496X.2017.1358608","article-title":"The Explanatory Power and the Forecast Performance of Consumer Confidence Indices for Private Consumption Growth in Turkey","volume":"54","year":"2018","journal-title":"Emerg. Mark. Financ. Trade"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1177\/1094670513513925","article-title":"The Role of Consumer Confidence in Creating Customer Loyalty","volume":"17","author":"Ou","year":"2013","journal-title":"J. Serv. Res."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jocs.2010.12.007","article-title":"Twitter mood predicts the stock market","volume":"2","author":"Bollen","year":"2011","journal-title":"J. Comput. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.ijhm.2015.07.010","article-title":"Understanding the impact of changes in consumer confidence on hotel stock performance in Taiwan","volume":"50","author":"Chen","year":"2015","journal-title":"Int. J. Hosp. Manag."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1007\/s10551-005-4667-2","article-title":"Business Ethics Index: Measuring Consumer Sentiments Toward Business Ethical Practices","volume":"64","author":"Tsalikis","year":"2006","journal-title":"J. Bus. Ethics"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1343","DOI":"10.1257\/aer.102.4.1343","article-title":"Information, Animal Spirits, and the Meaning of Innovations in Consumer Confidence","volume":"102","author":"Barsky","year":"2012","journal-title":"Am. Econ. Rev."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1353\/mcb.2004.0007","article-title":"Expectations, Heterogeneous Forecast Errors, and Consumption: Micro Evidence from the Michigan Consumer Sentiment Surveys","volume":"36","author":"Souleles","year":"2004","journal-title":"J. Money Credit Bank."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"61","DOI":"10.2307\/2534582","article-title":"The Buffer-Stock Theory of Saving: Some Macroeconomic Evidence","volume":"1992","author":"Carroll","year":"1992","journal-title":"Brook. Pap. Econ. Act."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1080\/1351847X.2014.963634","article-title":"Consumer confidence indices and stock markets\u2019 meltdowns","volume":"22","author":"Ferrer","year":"2016","journal-title":"Eur. J. Financ."},{"key":"ref_37","unstructured":"Snykers, G., Crismer, J., and Iania, L. (2023). How Does the Consumer Sentiment Index Shape Market Performance? A Comparative Analysis Across Heterogeneity in the USA over Time. [Master\u2019s Thesis, Universit\u00e9 catholique de Louvain]."},{"key":"ref_38","first-page":"2","article-title":"The influence of consumer confidence on inter-format competition: An analysis based on the French Consumer Confidence Index","volume":"32","author":"Ngobo","year":"2017","journal-title":"Rech. Appl. Mark. (Engl. Ed.)"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/S0165-1765(02)00292-6","article-title":"The stock market and consumer confidence: European evidence","volume":"79","author":"Jansen","year":"2003","journal-title":"Econ. Lett."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"19","DOI":"10.35536\/lje.2021.v26.i2.a2","article-title":"The Impact of Economic Policy Uncertainty on Consumer Confidence in Pakistan","volume":"26","author":"Shah","year":"2021","journal-title":"Lahore J. Econ."},{"key":"ref_41","first-page":"13","article-title":"Determinants of Consumer Spending Behavior During Economic Recessions","volume":"8","author":"Tai","year":"2024","journal-title":"Am. J. Econ. Sociol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1891\/1052-3073.28.1.49","article-title":"Spending Behavior Change and Financial Distress during the Great Recession","volume":"28","author":"Chalise","year":"2017","journal-title":"J. Financ. Couns. Plan."},{"key":"ref_43","unstructured":"Rose, G., and De Luca, D. (2024). Health Concerns and Consumption Expectations during COVID-19: Evidence from a Fuzzy Regression Discontinuity Design, Universit\u00e0 della Calabria, Dipartimento di Economia, Statistica e Finanza."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2809","DOI":"10.1681\/ASN.2018070759","article-title":"Genetic Analysis of 400 Patients Refines Understanding and Implicates a New Gene in Atypical Hemolytic Uremic Syndrome","volume":"29","author":"Bu","year":"2018","journal-title":"J. Am. Soc. Nephrol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"124470","DOI":"10.1016\/j.conbuildmat.2021.124470","article-title":"Development of data-driven prediction model for CFRP-steel bond strength by implementing ensemble learning algorithms","volume":"303","author":"Chen","year":"2021","journal-title":"Constr. Build. Mater."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1108\/JES-12-2020-0582","article-title":"Effects of economic policy uncertainty and political uncertainty on business confidence and investment","volume":"49","author":"Montes","year":"2022","journal-title":"J. Econ. Stud."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Shayaa, S., Sulaiman, A., Wai, P.S., Ashraf, M., Jaafar, N.I., Zakaria, S.B., Bahri Zakaria, S., Seuk Wai, P., and Wai Chung, Y. (2018). Consumer Confidence Index Predict Behavioral Intention to Purchase, Future Academy. European Proceedings of Social and Behavioural Sciences.","DOI":"10.15405\/epsbs.2018.07.02.80"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"691","DOI":"10.1111\/joes.12354","article-title":"More than a feeling: Confidence, uncertainty, and macroeconomic fluctuations","volume":"34","author":"Nowzohour","year":"2020","journal-title":"J. Econ. Surv."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1055","DOI":"10.1108\/IJHMA-09-2018-0070","article-title":"Impact of macroeconomic indicators on housing prices","volume":"12","author":"Mohan","year":"2019","journal-title":"Int. J. Hous. Mark. Anal."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Teresiene, D., Keliuotyte-Staniuleniene, G., Liao, Y., Kanapickiene, R., Pu, R., Hu, S., and Yue, X.-G. (2021). The Impact of the COVID-19 Pandemic on Consumer and Business Confidence Indicators. J. Risk Financ. Manag., 14.","DOI":"10.3390\/jrfm14040159"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"7","DOI":"10.30525\/2256-0742\/2022-8-3-7-13","article-title":"Consumer confidence and real economic growth in the Eurozone","volume":"8","author":"Borisov","year":"2022","journal-title":"Balt. J. Econ. Stud."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1108\/REPS-10-2021-0098","article-title":"US consumers\u2019 confidence and responses to COVID-19 shock","volume":"8","author":"Elmassah","year":"2023","journal-title":"Rev. Econ. Polit. Sci."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1293","DOI":"10.1108\/JES-11-2017-0326","article-title":"Macroeconomic effects of consumer confidence shock\u2014Evidence for state dependence","volume":"46","author":"Ahmad","year":"2019","journal-title":"J. Econ. Stud."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1","DOI":"10.21608\/sis.2021.40346.1006","article-title":"Economic and Social Factors Affecting the Purchasing Power of Customers in Fast Food Restaurants (Applied in Marsa Matrouh City)","volume":"2","author":"Shebl","year":"2021","journal-title":"J. Tour. Hotel. Herit."},{"key":"ref_55","first-page":"25","article-title":"Asymmetric exchange rate pass-through and sectoral stock price indices: Evidence from Turkey","volume":"7","author":"Benli","year":"2019","journal-title":"Int. J. Bus. Manag."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"168","DOI":"10.5296\/ber.v12i2.19829","article-title":"The Impact of World Food Price on Domestic Inflation: Evidence from Sri Lanka","volume":"12","author":"Anusha","year":"2022","journal-title":"Bus. Econ. Res."},{"key":"ref_57","unstructured":"Kallio, A. (2024, September 09). Economic Policy Uncertainty and Consumer Sentiment: Insights from Different Age Demographics. Available online: http:\/\/urn.fi\/URN:NBN:fi:jyu-202406174740."},{"key":"ref_58","first-page":"65","article-title":"The relationship between credit card expenditures, consumer confidence and consumers\u2019 saving tendencies","volume":"4","year":"2022","journal-title":"J. Empir. Econ. Soc. Sci."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1177\/00222437211060359","article-title":"The Impact of Historical Price Information on Purchase Deferral","volume":"59","author":"Gunadi","year":"2021","journal-title":"J. Mark. Res."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"44","DOI":"10.9734\/ajeba\/2023\/v23i8953","article-title":"How Do We Perceive Prices? A Three-category Taxonomy of Reference Price Effect on Consumers\u2019 Price Judgments","volume":"23","author":"Lii","year":"2023","journal-title":"Asian J. Econ. Bus. Acc."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1108\/EJMBE-12-2020-0344","article-title":"The mediating effect of consumers\u2019 price level perception and emotions towards supermarkets","volume":"31","author":"Cakici","year":"2022","journal-title":"Eur. J. Manag. Bus. Econ."},{"key":"ref_62","first-page":"219","article-title":"Is purchasing managers\u2019 index (pmi) a leading indicator for stock, bond and foreign exchange markets in turkey?","volume":"21","author":"Alsu","year":"2020","journal-title":"Dokuz Eyl\u00fcl \u00dcniv. \u0130\u015flet. Fak. Derg."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1016\/j.jbankfin.2014.06.017","article-title":"Forecasting US recessions: The role of sentiment","volume":"49","author":"Christiansen","year":"2014","journal-title":"J. Bank. Financ."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"741","DOI":"10.3846\/tede.2023.18705","article-title":"What drives China\u2019s long-term economic growth trend? A re-measurement based on a time-varying mixed-frequency dynamic factor model","volume":"29","author":"Liu","year":"2023","journal-title":"Technol. Econ. Dev. Econ."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1229","DOI":"10.1007\/s11205-017-1697-y","article-title":"The Effects of Unemployment and Insecure Jobs on Well-Being and Health: The Moderating Role of Labor Market Policies","volume":"138","author":"Gebel","year":"2018","journal-title":"Soc. Indic. Res."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1499","DOI":"10.1002\/fut.22444","article-title":"The effect of macroeconomic news announcements on the implied volatility of commodities: The role of survey releases","volume":"43","year":"2023","journal-title":"J. Futures Mark."},{"key":"ref_67","first-page":"16","article-title":"A Dynamic Relationship between Consumer Confidence and Residential Property Price: Empirical Evidence for Malaysia","volume":"11","author":"Ismail","year":"2021","journal-title":"Intern. J. Prop. Sci."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"317","DOI":"10.3846\/1648715X.2014.969793","article-title":"Does real estate transparency matter for foreign real estate investments?","volume":"18","author":"Farzanegan","year":"2014","journal-title":"Int. J. Strateg. Prop. Manag."},{"key":"ref_69","first-page":"121","article-title":"Money supply and inflation impact on economic growth","volume":"12","year":"2020","journal-title":"J. Financ. Econ. Policy"},{"key":"ref_70","first-page":"108","article-title":"Monetary Policy, Trade Openness and Economic Growth in India Under Monetary-targeting and Multiple-indicator Approach Regimes","volume":"19","author":"Hossain","year":"2019","journal-title":"J. Econ. Theory Pract."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"21582440221114324","DOI":"10.1177\/21582440221114324","article-title":"United States Tax Rates and Economic Growth","volume":"12","author":"Peterson","year":"2022","journal-title":"Sage Open"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Lin, Y.C., and Park, S.D. (2023). Effects of FDI, External Trade, and Human Capital of the ICT Industry on Sustainable Development in Taiwan. Sustainability, 15.","DOI":"10.3390\/su151411467"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Park, S.-D. (2018). The Nexus of FDI, R&D, and Human Capital on Chinese Sustainable Development: Evidence from a Two-Step Approach. Sustainability, 10.","DOI":"10.3390\/su10062063"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"2100349","DOI":"10.1002\/masy.202100349","article-title":"Modeling Consumer Price Index: A Machine Learning Approach","volume":"401","author":"Sarangi","year":"2022","journal-title":"Macromol. Symp."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"1166","DOI":"10.1029\/2018SW002061","article-title":"The challenge of machine learning in space weather: Nowcasting and forecasting","volume":"17","author":"Camporeale","year":"2019","journal-title":"Space Weather"},{"key":"ref_76","first-page":"219","article-title":"Thought confidence: Consumer emotions and the confidence premise hypothesis","volume":"4","author":"Chou","year":"2016","journal-title":"Manag. Stud."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1007\/s11205-016-1376-4","article-title":"Does Consumer Confidence Forecast Household Saving and Borrowing Behavior? Evidence for Poland","volume":"133","year":"2017","journal-title":"Soc. Indic. Res."},{"key":"ref_78","first-page":"27","article-title":"Consumer Confidence Linkages among European Union Countries: Cluster Analysis","volume":"12","author":"Maditinos","year":"2018","journal-title":"Appl. Econ."},{"key":"ref_79","first-page":"41","article-title":"Development of a Consumer Confidence Index for the Bangladesh Economy and Identification of Financial Risk Factors Affecting Consumer Confidence","volume":"15","author":"Khan","year":"2018","journal-title":"AIUB J. Bus. Econ."},{"key":"ref_80","first-page":"279","article-title":"Preliminary Data Linking American Consumer Perceptions with Unauthorized Migration to the U.S","volume":"20","author":"Venta","year":"2019","journal-title":"J. Int. Migr. Integr."},{"key":"ref_81","first-page":"173","article-title":"The Relationship Between Selected Financial and Macroeconomic Variables with Consumer Confidence Index","volume":"14","author":"Bicil","year":"2019","journal-title":"Ya\u015far \u00dcniv. E-Dergisi"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"101880","DOI":"10.1016\/j.foodpol.2020.101880","article-title":"Consumer trust in the food value chain and its impact on consumer confidence: A model for assessing consumer trust and evidence from a 5-country study in Europe","volume":"92","author":"Macready","year":"2020","journal-title":"Food Policy"},{"key":"ref_83","unstructured":"Giammanco, M.D., and Gitto, L. (2024, September 09). Government Measures and Economic Activity During the COVID-19 Outbreak: Some Preliminary Short-Term Evidence from Europe. Available online: http:\/\/dspace.wunu.edu.ua\/handle\/316497\/42211."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"1261","DOI":"10.1007\/s11135-020-01053-y","article-title":"The causal nexus of geopolitical risks, consumer and producer confidence indexes: Evidence from selected economies","volume":"55","author":"Alola","year":"2021","journal-title":"Qual. Quant."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"100413","DOI":"10.1016\/j.cosrev.2021.100413","article-title":"A systematic literature review on machine learning applications for consumer sentiment analysis using online reviews","volume":"41","author":"Jain","year":"2021","journal-title":"Comput. Sci. Rev."},{"key":"ref_86","first-page":"71","article-title":"Forecasting the GDP Growth in Pakistan: The Role of Consumer Confidence","volume":"27","author":"Shah","year":"2022","journal-title":"Lahore J. Econ."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Han, H., Li, Z., and Li, Z. (2023). Using Machine Learning Methods to Predict Consumer Confidence from Search Engine Data. Sustainability, 15.","DOI":"10.3390\/su15043100"},{"key":"ref_88","first-page":"1","article-title":"Backward assessments or expectations: What determines the consumer confidence index more strongly? Panel model based on the CCI of European countries","volume":"68","year":"2023","journal-title":"Wiad. Stat. Pol. Stat."},{"key":"ref_89","unstructured":"Vitkauskait\u0117, A. (2024). Evaluation of Consumer Confidence Indicators Using Social Media and Administrative Data. [Master\u2019s Thesis, Vilniaus Universitetas]."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"122945","DOI":"10.1016\/j.techfore.2023.122945","article-title":"Extreme gradient boosting trees with efficient Bayesian optimization for profit-driven customer churn prediction","volume":"198","author":"Liu","year":"2024","journal-title":"Technol. Forecast. Soc. Chang."},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Liu, Z., De Bock, K.W., and Zhang, L. (2024). Explainable Profit-Driven Hotel Booking Cancellation Prediction based on Heterogeneous Stacking-Based Ensemble Classification. Eur. J. Oper. Res.","DOI":"10.1016\/j.ejor.2024.08.026"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"101928","DOI":"10.1016\/j.jocs.2022.101928","article-title":"A broad approach to expert detection using syntactic and semantic social networks analysis in the context of Global Software Development","volume":"66","author":"Lopes","year":"2023","journal-title":"J. Comput. Sci."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"112792","DOI":"10.1016\/j.rser.2022.112792","article-title":"Evaluating and improving social awareness of energy communities through semantic network analysis of online news","volume":"167","author":"Piselli","year":"2022","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"101598","DOI":"10.1016\/j.poetic.2021.101598","article-title":"Meaning structures in the world polity: A semantic network analysis of human rights terminology in the world\u2019s peace agreements","volume":"88","author":"Puetz","year":"2021","journal-title":"Poetics"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.joi.2009.11.003","article-title":"Diffusion of latent semantic analysis as a research tool: A social network analysis approach","volume":"4","author":"Tonta","year":"2010","journal-title":"J. Informetr."},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Danowski, J., Riopelle, K., and Yan, B. (2024). Cascaded Semantic Fractionation for identifying a domain in social media. Front. Res. Metr. Anal., 9.","DOI":"10.3389\/frma.2024.1189099"},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Vijiyakumar, K., Muruganandhan, D., and Rajesh, V. (2023, January 17\u201318). Investigating the Fake News Using Machine Learning Algorithms. Proceedings of the 2023 International Conference on System, Computation, Automation and Networking (ICSCAN), Puducherry, India.","DOI":"10.1109\/ICSCAN58655.2023.10394967"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"3932","DOI":"10.1073\/pnas.1517384113","article-title":"Discovering governing equations from data by sparse identification of nonlinear dynamical systems","volume":"113","author":"Brunton","year":"2016","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Kocijan, J. (2016). Modelling and Control of Dynamic Systems Using Gaussian Process Models, Springer.","DOI":"10.1007\/978-3-319-21021-6"},{"key":"ref_100","unstructured":"Goodfellow, I. (2016). Deep Learning, The MIT Press."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"119795","DOI":"10.1016\/j.renene.2023.119795","article-title":"A hybrid AHP-PROMETHEE II onshore wind farms multicriteria suitability analysis using kNN and SVM regression models in northeastern Greece","volume":"221","author":"Sotiropoulou","year":"2024","journal-title":"Renew. Energy"},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"1188","DOI":"10.1109\/72.870050","article-title":"Improvements to the SMO algorithm for SVM regression","volume":"11","author":"Shevade","year":"2000","journal-title":"IEEE Trans. Neural. Netw."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"3703","DOI":"10.1039\/c1cs15008a","article-title":"Recent advances in catalytic hydrogenation of carbon dioxide","volume":"40","author":"Wang","year":"2011","journal-title":"Chem. Soc. Rev."},{"key":"ref_104","unstructured":"Saunders, C., Stitson, M.O., Weston, J., Bottou, L., and Smola, A. (2024, September 09). Support Vector Machine-Reference Manual. Available online: https:\/\/eprints.soton.ac.uk\/258959\/1\/SVM_Reference.pdf."},{"key":"ref_105","first-page":"281","article-title":"Support vector method for function approximation, regression estimation and signal processing","volume":"9","author":"Vapnik","year":"1996","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/j.measurement.2014.01.035","article-title":"An SVM approach with electromagnetic methods to assess metal plate thickness","volume":"54","author":"Ramos","year":"2014","journal-title":"Measurement"},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1016\/j.ijepes.2012.07.057","article-title":"Transient stability assessment of power system using support vector machine with generator combinatorial trajectories inputs","volume":"44","author":"You","year":"2013","journal-title":"Int. J. Electr. Power"},{"key":"ref_108","unstructured":"Hamel, L.H. (2011). Knowledge Discovery with Support Vector Machines, John Wiley & Sons."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1023\/B:STCO.0000035301.49549.88","article-title":"A tutorial on support vector regression","volume":"14","author":"Smola","year":"2004","journal-title":"Stat. Comput."},{"key":"ref_110","doi-asserted-by":"crossref","unstructured":"Braga, I., do Carmo, L.P., Benatti, C.C., and Monard, M.C. (2013). A Note on Parameter Selection for Support Vector Machines. Advances in Soft Computing and Its Applications, Springer.","DOI":"10.1007\/978-3-642-45111-9_21"},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v015.i09","article-title":"Support Vector Machines in R","volume":"15","author":"Karatzoglou","year":"2006","journal-title":"J. Stat. Softw."},{"key":"ref_112","unstructured":"Mechelli, A., and Vieira, S. (2019). Machine Learning: Methods and Applications to Brain Disorders, Academic Press."},{"key":"ref_113","unstructured":"Segal, M.R. (2024, September 09). Machine Learning Benchmarks and Random Forest Regression. Available online: https:\/\/escholarship.org\/uc\/item\/35x3v9t4."},{"key":"ref_114","doi-asserted-by":"crossref","unstructured":"Caruana, R., and Niculescu-Mizil, A. (2006, January 25\u201329). An empirical comparison of supervised learning algorithms. Proceedings of the 23rd International Conference on Machine Learning, Pittsburgh, PA, USA.","DOI":"10.1145\/1143844.1143865"},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"e041223224189","DOI":"10.2174\/0118722121248202231003064459","article-title":"Application of Machine Learning Predicting Injuries in Traffic Accidents through the Application of Random Forest","volume":"19","author":"Singh","year":"2025","journal-title":"Recent Pat. Eng."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy Function Approximation: A Gradient Boosting Machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1007\/BF00116251","article-title":"Induction of decision trees","volume":"1","author":"Quinlan","year":"1986","journal-title":"Mach. Learn."},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/S0167-9473(01)00065-2","article-title":"Stochastic gradient boosting","volume":"38","author":"Friedman","year":"2002","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1007\/s12083-023-01597-4","article-title":"Unveiling DoH tunnel: Toward generating a balanced DoH encrypted traffic dataset and profiling malicious behavior using inherently interpretable machine learning","volume":"17","author":"Niktabe","year":"2024","journal-title":"Peer Peer Netw. Appl."},{"key":"ref_120","doi-asserted-by":"crossref","unstructured":"Youssef, A., Mishra, P., Vitale, M., Schillaci, G., Veneri, G., Bettini, A., Anatriello, G., Burbui, M., and Ceccherini, F. (2024, January 18\u201320). Online Sequence-Based Deep Learning Approach for Metallic Debossed and Embossed Turbomachinery Blade Text Recognition Application. Proceedings of the International Petroleum Technology Conference, Kuala Lumpur, Malaysia.","DOI":"10.2523\/IPTC-23115-MS"},{"key":"ref_121","first-page":"104","article-title":"Inflation Anchoring in India","volume":"7","author":"Punam","year":"2024","journal-title":"J. Financ. Plann. Manag."},{"key":"ref_122","unstructured":"Shahabadi, A., Omidi, V., and Jahandideh, M. (2024). The Impact of Various Inequalities on Social Unrest in Middle Eastern Countries. Macroecon. Res. Lett., 19."},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"100034","DOI":"10.1016\/j.jclimf.2024.100034","article-title":"Climate Change and Volatility Forecasting: Novel Insights from Sectoral Indices","volume":"6","author":"Ghani","year":"2024","journal-title":"J. Clim. Financ."},{"key":"ref_124","first-page":"183","article-title":"Consumer Confidence and Economic Activity: What Causes What?: What Causes What?","volume":"17","author":"Kim","year":"2016","journal-title":"Korea World Econ."},{"key":"ref_125","unstructured":"Utami, F. (2023). Pengaruh Inflasi, Tingkat Suku Bunga dan E-Money Terhadap Jumlah Uang Beredar di Indonesia, Universitas Medan Area."},{"key":"ref_126","first-page":"829","article-title":"Analyzing the Impact of Consumer Confidence Index and Geopolitical Risk Indices on Foreign Trade in Food Commodities: Evidence from Turkey","volume":"12","author":"Karabulut","year":"2022","journal-title":"Int. J. Contemp. Econ. Adm. Sci."},{"key":"ref_127","first-page":"103","article-title":"Consumer confidence, stock prices and exchange rates: The case of Turkey","volume":"10","year":"2010","journal-title":"Appl. Econom. Int. Dev."},{"key":"ref_128","doi-asserted-by":"crossref","unstructured":"Tsai, P.W., Liu, C.H., Liao, L.C., and Chang, J.F. (2015, January 23\u201325). Using Consumer Confidence Index in the Foreign Exchange Rate Forecasting. Proceedings of the 2015 International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP), Adelaide, Australia.","DOI":"10.1109\/IIH-MSP.2015.36"},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"103227","DOI":"10.1016\/j.resourpol.2022.103227","article-title":"Chinese crude oil futures volatility and sustainability: An uncertainty indices perspective","volume":"80","author":"Huang","year":"2023","journal-title":"Resour. Policy"},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1016\/0306-4573(88)90021-0","article-title":"Term-weighting approaches in automatic text retrieval","volume":"24","author":"Salton","year":"1988","journal-title":"Inf. Process. Manag."},{"key":"ref_131","unstructured":"(2023, January 10). Analytic Technologies. Available online: http:\/\/www.analytictech.com\/archive\/ucinet.htm."},{"key":"ref_132","doi-asserted-by":"crossref","unstructured":"Lu, Y., and Park, S.D. (2022). Time Series Analysis of Policy Discourse on Green Consumption in China: Text Mining and Network Analysis. Sustainability, 14.","DOI":"10.3390\/su142214704"},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"111043","DOI":"10.1016\/j.envres.2021.111043","article-title":"The structural equivalence of tourism cooperative network in the Belt and Road Initiative Area","volume":"197","author":"Bai","year":"2021","journal-title":"Environ. Res."},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.ijcard.2017.06.073","article-title":"Impact of atrial arrhythmias on outcome in adults with congenital heart disease","volume":"248","author":"Yang","year":"2017","journal-title":"Int. J. Cardiol."},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"424","DOI":"10.2307\/1912791","article-title":"Investigating Causal Relations by Econometric Models and Cross-spectral Methods","volume":"37","author":"Granger","year":"1969","journal-title":"Econometrica"},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"111714","DOI":"10.1016\/j.ecolind.2024.111714","article-title":"Mangrove extraction from super-resolution images generated by deep learning models","volume":"159","author":"Hong","year":"2024","journal-title":"Ecol. Indic."},{"key":"ref_137","doi-asserted-by":"crossref","unstructured":"Shao, Y., Feng, Z., Cao, M., Wang, W., Sun, L., Yang, X., Ma, T., Guo, Z., Fahad, S., and Liu, X. (2023). An Ensemble Model for Forest Fire Occurrence Mapping in China. Forests, 14.","DOI":"10.3390\/f14040704"},{"key":"ref_138","doi-asserted-by":"crossref","unstructured":"Duan, H., Zhang, Y., Qiu, H., Fu, X., Liu, C., Zang, X., Xu, A., Wu, Z., Li, X., and Zhang, Q. (2024). Machine learning-based prediction model for distant metastasis of breast cancer. Comput. Biol. Med., 169.","DOI":"10.1016\/j.compbiomed.2024.107943"},{"key":"ref_139","doi-asserted-by":"crossref","unstructured":"Luo, Z., Wang, H., and Li, S. (2022). Prediction of International Roughness Index Based on Stacking Fusion Model. Sustainability, 14.","DOI":"10.3390\/su14126949"},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1002\/hbe2.156","article-title":"Building consumer confidence index based on social media big data","volume":"1","author":"Wang","year":"2019","journal-title":"Hum. Behav. Emerg. Technol."},{"key":"ref_141","doi-asserted-by":"crossref","first-page":"102755","DOI":"10.1016\/j.irfa.2023.102755","article-title":"A two-stage credit scoring model based on random forest: Evidence from Chinese small firms","volume":"89","author":"Zhou","year":"2023","journal-title":"Int. Rev. Financ. Anal."},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"05024002","DOI":"10.1061\/JCEMD4.COENG-13915","article-title":"Duration Estimation of a Heavy Industrial Scaffolding Project: A Case Study","volume":"150","author":"Rizaee","year":"2024","journal-title":"J. Constr. Eng. Manag."},{"key":"ref_143","doi-asserted-by":"crossref","unstructured":"Taplin, R. (2023). Investigating Causes of Model Instability: Properties of the Prediction Accuracy Index. Risks, 11.","DOI":"10.3390\/risks11060110"},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"108871","DOI":"10.1016\/j.jobe.2024.108871","article-title":"Effects of material properties uncertainty on seismic fragility of reinforced-concrete frames using machine learning approach","volume":"86","author":"Latif","year":"2024","journal-title":"J. Build. Eng."},{"key":"ref_145","doi-asserted-by":"crossref","first-page":"1120","DOI":"10.1016\/j.ijforecast.2015.12.011","article-title":"Cross-validation aggregation for combining autoregressive neural network forecasts","volume":"32","author":"Barrow","year":"2016","journal-title":"Int. J. Forecast."},{"key":"ref_146","doi-asserted-by":"crossref","first-page":"3081","DOI":"10.1016\/j.eswa.2007.06.037","article-title":"Forecasting financial condition of Chinese listed companies based on support vector machine","volume":"34","author":"Ding","year":"2008","journal-title":"Expert Syst. Appl."},{"key":"ref_147","doi-asserted-by":"crossref","unstructured":"Luo, J., and Fu, Y. (2018, January 20\u201322). Predicting China\u2019s Economic Running State Using Machine Learning. Proceedings of the MATEC Web of Conferences, Abu Dhabi, United Arab Emirates.","DOI":"10.1051\/matecconf\/201823203036"},{"key":"ref_148","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.jfineco.2021.08.017","article-title":"Machine learning in the Chinese stock market","volume":"145","author":"Leippold","year":"2022","journal-title":"J. Financ. Econ."},{"key":"ref_149","first-page":"115","article-title":"Understanding consumer confidence evolution in response to adverse shocks and how marketing contributes to business success in economic downturns","volume":"10","year":"2023","journal-title":"J. Manag. Mark. Logist."},{"key":"ref_150","first-page":"499","article-title":"Intelligent PITB Trust Blockchain Model of Sentiment Analysis for the Decision-Making of Taverns Dynamic Recommendation System in China","volume":"12","author":"Zhou","year":"2024","journal-title":"Int. J. Intell. Syst. Appl. Eng."}],"container-title":["Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-8954\/12\/11\/445\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:18:13Z","timestamp":1760113093000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-8954\/12\/11\/445"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,23]]},"references-count":150,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2024,11]]}},"alternative-id":["systems12110445"],"URL":"https:\/\/doi.org\/10.3390\/systems12110445","relation":{},"ISSN":["2079-8954"],"issn-type":[{"value":"2079-8954","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,23]]}}}