{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T12:20:44Z","timestamp":1773490844642,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":21,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819570744","type":"print"},{"value":"9789819570751","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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\/978-981-95-7075-1_35","type":"book-chapter","created":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T11:13:15Z","timestamp":1773486795000},"page":"537-548","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MACT: Mutation-Aware CNN-Transformer for ESG Forecasting"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-3932-1497","authenticated-orcid":false,"given":"Xie","family":"Yuxuan","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0009-7050-6991","authenticated-orcid":false,"given":"Yang","family":"Bochuang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0000-4191-1423","authenticated-orcid":false,"given":"Xie","family":"Yuxin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,3,15]]},"reference":[{"issue":"6","key":"35_CR1","doi-asserted-by":"publisher","first-page":"2377","DOI":"10.3390\/su16062377","volume":"16","author":"K Yang","year":"2024","unstructured":"Yang, K., Zhang, T., Ye, C.: The sustainability of corporate ESG performance: an empirical study. Sustainability 16(6), 2377 (2024)","journal-title":"Sustainability"},{"key":"35_CR2","doi-asserted-by":"publisher","first-page":"S119","DOI":"10.1016\/j.bir.2022.11.006","volume":"22","author":"M Aydo\u011fmu\u015f","year":"2022","unstructured":"Aydo\u011fmu\u015f, M., G\u00fclay, G., Ergun, K.: Impact of ESG performance on firm value and profitability. Borsa Istanbul Rev. 22, S119\u2013S127 (2022)","journal-title":"Borsa Istanbul Rev."},{"key":"35_CR3","doi-asserted-by":"crossref","unstructured":"de Souza Barbosa, A., da Silva, M.C.B.C., da Silva, L.B., Morioka, S.N., de Souza, V.F.: Integration of Environmental, Social, and Governance (ESG) criteria: their impacts on corporate sustainability performance. Human. Soc. Sci. Commun. 10(1), 1\u201318 (2023)","DOI":"10.1057\/s41599-023-01919-0"},{"key":"35_CR4","doi-asserted-by":"crossref","unstructured":"Aue, T., Jatowt, A., F\u00e4rber, M.: Predicting company ESG ratings from news articles using multivariate timeseries analysis. In: Companion Proceedings of the ACM on Web Conference 2025, pp. 1774\u20131780. ACM, New York (2025)","DOI":"10.1145\/3701716.3717509"},{"key":"35_CR5","doi-asserted-by":"publisher","first-page":"1371616","DOI":"10.3389\/fenrg.2024.1371616","volume":"12","author":"S Chen","year":"2024","unstructured":"Chen, S., Fan, M.: ESG ratings and corporate success: analyzing the environmental governance impact on Chinese companies\u2019 performance. Front. Energy Res. 12, 1371616 (2024)","journal-title":"Front. Energy Res."},{"issue":"4","key":"35_CR6","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1080\/20430795.2015.1118917","volume":"5","author":"G Friede","year":"2015","unstructured":"Friede, G., Busch, T., Bassen, A.: ESG and financial performance: aggregated evidence from more than 2000 empirical studies. J. Sustainable Finance & Investment 5(4), 210\u2013233 (2015)","journal-title":"J. Sustainable Finance & Investment"},{"key":"35_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.jenvman.2023.118829","volume":"345","author":"S Chen","year":"2023","unstructured":"Chen, S., Song, Y., Gao, P.: Environmental, social, and governance (ESG) performance and financial outcomes: Analyzing the impact of ESG on financial performance. J. Environ. Manage. 345, 118829 (2023)","journal-title":"J. Environ. Manage."},{"issue":"1","key":"35_CR8","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1186\/s40854-023-00604-0","volume":"10","author":"HN Bhandari","year":"2024","unstructured":"Bhandari, H.N., Pokhrel, N.R., Rimal, R., Dahal, K.R., Rimal, B.: Implementation of deep learning models in predicting ESG index volatility. Financial Innovation 10(1), 75 (2024)","journal-title":"Financial Innovation"},{"issue":"8","key":"35_CR9","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"issue":"2","key":"35_CR10","doi-asserted-by":"publisher","first-page":"410","DOI":"10.3390\/math11020410","volume":"11","author":"SL Lin","year":"2023","unstructured":"Lin, S.L., Jin, X.: Does ESG predict systemic banking crises? a computational economics model of early warning systems with interpretable multi-variable LSTM based on mixture attention. Mathematics 11(2), 410 (2023)","journal-title":"Mathematics"},{"key":"35_CR11","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N. et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30, pp. 5998\u20136008. Curran Associates, Red Hook (2017)"},{"key":"35_CR12","doi-asserted-by":"crossref","unstructured":"Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 12, pp. 11106\u201311115. AAAI Press, Palo Alto (2021)","DOI":"10.1609\/aaai.v35i12.17325"},{"key":"35_CR13","unstructured":"Wu, H., Xu, J., Wang, J., Long, M.: Autoformer: decomposition transformers with auto-correlation for long-term series forecasting. In: Advances in Neural Information Processing Systems, vol. 34, pp. 22419\u201322430. Curran Associates, Red Hook (2021)"},{"key":"35_CR14","unstructured":"Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T.,\u00a0 Veloso, M.: Financial time series forecasting using cnn and transformer.\u00a0arXiv preprint arXiv:2304.04912 (2023)"},{"key":"35_CR15","unstructured":"Tu, T.: Bridging Short-and Long-Term Dependencies: A CNN-Transformer Hybrid for Financial Time Series Forecasting.\u00a0arXiv preprint arXiv:2504.19309 (2025)"},{"key":"35_CR16","doi-asserted-by":"crossref","unstructured":"El Zaar, A., Mansouri, A., Benaya, N., Bakir, T., El Allati, A.: Hybrid Transformer-CNN architecture for multivariate time series forecasting: Integrating attention mechanisms with convolutional feature extraction. Journal of Intelligent Information Systems, pp. 1\u201332 (2025)","DOI":"10.1007\/s10844-025-00937-5"},{"key":"35_CR17","doi-asserted-by":"crossref","unstructured":"Xie, Y., Chen, X., Zhang, L.: Prediction of PM2.5 Concentration Based on CNNLSTM Deep Learning Model. In: 2023 Asia-Europe Conference on Electronics. Data Processing and Informatics (ACEDPI), pp. 229\u2013233. IEEE, Piscataway (2023)","DOI":"10.1109\/ACEDPI58926.2023.00051"},{"key":"35_CR18","doi-asserted-by":"crossref","unstructured":"Hu, J., Zheng, W.: Transformation-gated LSTM: efficient capture of short-term mutation dependencies for multivariate time series prediction tasks. In: 2019 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20138. IEEE, Piscataway (2019)","DOI":"10.1109\/IJCNN.2019.8852073"},{"key":"35_CR19","unstructured":"Zhao, K., He, Z., Hung, A., Zeng, D.: Dominant Shuffle: A Simple Yet Powerful Data Augmentation for Time-series Prediction. arXiv preprint arXiv:2405.16456 (2024)"},{"issue":"1","key":"35_CR20","doi-asserted-by":"publisher","first-page":"4890","DOI":"10.1038\/s41598-024-55483-x","volume":"14","author":"K Cao","year":"2024","unstructured":"Cao, K., Zhang, T., Huang, J.: Advanced hybrid LSTM-transformer architecture for real-time multi-task prediction in engineering systems. Sci. Rep. 14(1), 4890 (2024)","journal-title":"Sci. Rep."},{"issue":"1","key":"35_CR21","doi-asserted-by":"publisher","first-page":"11184","DOI":"10.1038\/s41598-024-62127-7","volume":"14","author":"W Li","year":"2024","unstructured":"Li, W., Liu, C., Hu, C., Niu, C., Li, R., Li, M., et al.: Application of a hybrid algorithm of LSTM and Transformer based on random search optimization for improving rainfall-runoff simulation. Sci. Rep. 14(1), 11184 (2024)","journal-title":"Sci. Rep."}],"container-title":["Lecture Notes in Computer Science","PRICAI 2025: Trends in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-7075-1_35","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T11:13:17Z","timestamp":1773486797000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-7075-1_35"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819570744","9789819570751"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-7075-1_35","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"15 March 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific Rim International Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Wellington","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"New Zealand","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":"17 November 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 November 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pricai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.pricai.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}