{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T18:48:48Z","timestamp":1777488528200,"version":"3.51.4"},"publisher-location":"Singapore","reference-count":27,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819530519","type":"print"},{"value":"9789819530526","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T00:00:00Z","timestamp":1763424000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T00:00:00Z","timestamp":1763424000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-981-95-3052-6_34","type":"book-chapter","created":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T22:07:44Z","timestamp":1763417264000},"page":"443-454","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Hybrid Kolmogorov-Arnold and\u00a0Graph Attention Networks for\u00a0Gold Price Forecasting Under Uncertainty"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-5038-8570","authenticated-orcid":false,"given":"Dat","family":"Le","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6559-6736","authenticated-orcid":false,"given":"Sutharshan","family":"Rajasegarar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4711-7543","authenticated-orcid":false,"given":"Wei","family":"Luo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9709-1663","authenticated-orcid":false,"given":"Thanh Thi","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0931-0916","authenticated-orcid":false,"given":"Maia","family":"Angelova","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,11,18]]},"reference":[{"key":"34_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2024.127966","volume":"597","author":"A Ceni","year":"2024","unstructured":"Ceni, A., Gallicchio, C.: Residual echo state networks: residual recurrent neural networks with stable dynamics and fast learning. Neurocomputing 597, 127966 (2024)","journal-title":"Neurocomputing"},{"key":"34_CR2","doi-asserted-by":"crossref","unstructured":"Challu, C., Olivares, K.G., Oreshkin, B., Ramirez, F., Canseco, M., Dubrawski, A.: NHITS: Neural hierarchical interpolation for time series forecasting. Proc. AAAI 37, 6989\u20136997 (2023)","DOI":"10.1609\/aaai.v37i6.25854"},{"issue":"3","key":"34_CR3","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1080\/07350015.1995.10524599","volume":"13","author":"F Diebold","year":"1995","unstructured":"Diebold, F., Mariano, R.: Comparing predictive accuracy. J. Bus. Econ. Stat. 13(3), 253\u2013263 (1995)","journal-title":"J. Bus. Econ. Stat."},{"key":"34_CR4","doi-asserted-by":"publisher","unstructured":"Elahi, U., Khalid, Z., Kennedy, R.A., McEwen, J.D.: Iterative residual fitting for spherical harmonic transform of band-limited signals on the sphere: Generalization and analysis. In: 2017 International Conference on Sampling Theory and Applications (SampTA), pp. 470\u2013474. IEEE (Jul 2017). https:\/\/doi.org\/10.1109\/sampta.2017.8024463","DOI":"10.1109\/sampta.2017.8024463"},{"key":"34_CR5","doi-asserted-by":"crossref","unstructured":"Gobato\u00a0Souto, H., Heuvel, S.K.: Tsmixer and realized volatility prediction. Available at SSRN (2024)","DOI":"10.2139\/ssrn.4713756"},{"key":"34_CR6","unstructured":"Goel, H., Melnyk, I., Banerjee, A.: R2N2: residual recurrent neural networks for multivariate time series forecasting. CoRR abs\/1709.03159 (2017). http:\/\/arxiv.org\/abs\/1709.03159"},{"key":"34_CR7","doi-asserted-by":"publisher","unstructured":"Han, J., Zeng, P.: Residual bilstm based hybrid model for short-term load forecasting in buildings. J. Build. Eng. 99, 111593 (2025). https:\/\/doi.org\/10.1016\/j.jobe.2024.111593, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2352710224031619","DOI":"10.1016\/j.jobe.2024.111593"},{"key":"34_CR8","doi-asserted-by":"publisher","first-page":"679","DOI":"10.1016\/j.ijforecast.2006.03.001","volume":"22","author":"R Hyndman","year":"2006","unstructured":"Hyndman, R., Koehler, A.: Another look at measures of forecast accuracy. Int. J. Forecast. 22, 679\u2013688 (2006)","journal-title":"Int. J. Forecast."},{"key":"34_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.122666","volume":"241","author":"S Islam","year":"2024","unstructured":"Islam, S., Elmekki, H., Elsebai, A., Bentahar, J., Drawel, N., Rjoub, G., Pedrycz, W.: A comprehensive survey on applications of transformers for deep learning tasks. Expert Syst. Appl. 241, 122666 (2024)","journal-title":"Expert Syst. Appl."},{"issue":"1","key":"34_CR10","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1007\/s11280-021-01003-0","volume":"26","author":"M Li","year":"2023","unstructured":"Li, M., Zhu, Y., Shen, Y., Angelova, M.: Clustering-enhanced stock price prediction using deep learning. World Wide Web 26(1), 207\u2013232 (2023)","journal-title":"World Wide Web"},{"key":"34_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.resourpol.2023.104279","volume":"88","author":"Y Li","year":"2024","unstructured":"Li, Y., Du, Q.: Oil price volatility and gold prices volatility asymmetric links with natural resources via financial market fluctuations: Implications for green recovery. Resour. Policy 88, 104279 (2024)","journal-title":"Resour. Policy"},{"key":"34_CR12","unstructured":"Liu, Z., et al.: Kan: Kolmogorov-arnold networks. arXiv preprint arXiv:2404.19756 (2024)"},{"key":"34_CR13","doi-asserted-by":"crossref","unstructured":"Majumder, M.M.R., Hossain, M.I.: Limitation of arima in extremely collapsed market: A proposed method. In: 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE), pp.\u00a01\u20135. IEEE (2019)","DOI":"10.1109\/ECACE.2019.8679216"},{"issue":"4","key":"34_CR14","first-page":"149","volume":"15","author":"V Mohammadi","year":"2024","unstructured":"Mohammadi, V., Fallah Shams, M.F., Zomorodian, G.: The gold market bubble and its contagion to the stock market. Int. J. Nonlinear Anal. Appl. 15(4), 149\u2013158 (2024)","journal-title":"Int. J. Nonlinear Anal. Appl."},{"key":"34_CR15","unstructured":"Oreshkin, B.N., Carpov, D., Chapados, N., Bengio, Y.: N-BEATS: Neural basis expansion analysis for interpretable time series forecasting. arXiv preprint arXiv:1905.10437 (2019)"},{"key":"34_CR16","unstructured":"Ren, J., Wu, S.: Two-stage hybrid models for enhancing forecasting accuracy on heterogeneous time series (2025). https:\/\/arxiv.org\/abs\/2502.08600"},{"key":"34_CR17","doi-asserted-by":"crossref","unstructured":"da\u00a0Silva, E.G., de\u00a0Mattos\u00a0Neto, P.S., de\u00a0Oliveira, J.F.: Hybrid system for time series using iterative residual forecasting models. In: 2019 8th Brazilian Conference on Intelligent Systems (BRACIS), pp. 872\u2013877. IEEE (2019)","DOI":"10.1109\/BRACIS.2019.00155"},{"key":"34_CR18","unstructured":"Somvanshi, S., Javed, S.A., Islam, M.M., Pandit, D., Das, S.: A survey on kolmogorov-arnold network. arXiv preprint arXiv:2411.06078 (2024)"},{"key":"34_CR19","doi-asserted-by":"crossref","unstructured":"Vaca-Rubio, C.J., Blanco, L., Pereira, R., Caus, M.: Kolmogorov-arnold networks (kans) for time series analysis. arXiv preprint arXiv:2405.08790 (2024)","DOI":"10.1109\/GCWkshp64532.2024.11100692"},{"key":"34_CR20","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)"},{"issue":"2","key":"34_CR21","doi-asserted-by":"publisher","first-page":"93","DOI":"10.3390\/a18020093","volume":"18","author":"I Viktoratos","year":"2025","unstructured":"Viktoratos, I., Tsadiras, A.: Advancing real-estate forecasting: A novel approach using kolmogorov-arnold networks. Algorithms 18(2), 93 (2025)","journal-title":"Algorithms"},{"issue":"9","key":"34_CR22","doi-asserted-by":"publisher","first-page":"318","DOI":"10.3390\/fi16090318","volume":"16","author":"AG Vrahatis","year":"2024","unstructured":"Vrahatis, A.G., Lazaros, K., Kotsiantis, S.: Graph attention networks: a comprehensive review of methods and applications. Future Internet 16(9), 318 (2024)","journal-title":"Future Internet"},{"key":"34_CR23","unstructured":"Wen, Q., et al.: Transformers in time series: a survey. arXiv preprint arXiv:2202.07125 (2022)"},{"issue":"1","key":"34_CR24","doi-asserted-by":"publisher","first-page":"79","DOI":"10.3354\/cr030079","volume":"30","author":"CJ Willmott","year":"2005","unstructured":"Willmott, C.J., Matsuura, K.: Advantages of the mean absolute error (mae) over the root mean square error (rmse) in assessing average model performance. Climate Res. 30(1), 79\u201382 (2005)","journal-title":"Climate Res."},{"issue":"1","key":"34_CR25","doi-asserted-by":"publisher","DOI":"10.1049\/tje2.70050","volume":"2025","author":"X Zeng","year":"2025","unstructured":"Zeng, X., Ji, G., Zhou, Y., Li, H., Wei, T.: Multi-load forecasting for integrated energy systems based on gat-mtl. J. Eng. 2025(1), e70050 (2025)","journal-title":"J. Eng."},{"issue":"6","key":"34_CR26","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0286770","volume":"18","author":"Z Zhang","year":"2023","unstructured":"Zhang, Z., Chen, Y., Wang, H., Fu, Q., Chen, J., Lu, Y.: Anomaly detection method for building energy consumption in multivariate time series based on graph attention mechanism. PLoS ONE 18(6), e0286770 (2023)","journal-title":"PLoS ONE"},{"issue":"8","key":"34_CR27","doi-asserted-by":"publisher","first-page":"3751","DOI":"10.1109\/TKDE.2024.3363703","volume":"36","author":"H Zhou","year":"2024","unstructured":"Zhou, H., He, T., Ong, Y.S., Cong, G., Chen, Q.: Differentiable clustering for graph attention. IEEE Trans. Knowl. Data Eng. 36(8), 3751\u20133764 (2024)","journal-title":"IEEE Trans. Knowl. Data Eng."}],"container-title":["Lecture Notes in Computer Science","Knowledge Science, Engineering and Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-3052-6_34","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T22:07:49Z","timestamp":1763417269000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-3052-6_34"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,18]]},"ISBN":["9789819530519","9789819530526"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-3052-6_34","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,18]]},"assertion":[{"value":"18 November 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"KSEM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Knowledge Science, Engineering and Management","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Macao","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 August 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 August 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ksem2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ksem2025.scimeeting.cn\/en\/web\/index\/27434","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}