{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T14:20:10Z","timestamp":1783952410323,"version":"3.55.0"},"publisher-location":"Singapore","reference-count":23,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819233830","type":"print"},{"value":"9789819233847","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T00:00:00Z","timestamp":1783987200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T00:00:00Z","timestamp":1783987200000},"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":[[2027]]},"DOI":"10.1007\/978-981-92-3384-7_29","type":"book-chapter","created":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T13:25:33Z","timestamp":1783949133000},"page":"337-349","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Temporal-Structure-Aware Enhancement Method for Industrial Process Forecasting"],"prefix":"10.1007","author":[{"given":"Mengge","family":"Ai","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuejin","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiuyu","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chang","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xi","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,14]]},"reference":[{"issue":"2","key":"29_CR1","doi-asserted-by":"publisher","first-page":"538","DOI":"10.3390\/en19020538","volume":"19","author":"J Eckhoff","year":"2026","unstructured":"Eckhoff, J., Wadhwa, S., Fette, M., Wulfsberg, J.P., Wanigasekara, C.: Electrical load forecasting in the industrial sector: a literature review of machine learning models and architectures for grid planning. Energies. 19(2), 538 (2026)","journal-title":"Energies"},{"issue":"1","key":"29_CR2","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1007\/s00170-024-13372-7","volume":"138","author":"H Wicaksono","year":"2025","unstructured":"Wicaksono, H., et al.: Artificial-intelligence-enabled dynamic demand response system for maximizing the use of renewable electricity in production processes. Int. J. Adv. Manuf. Technol. 138(1), 247\u2013271 (2025)","journal-title":"Int. J. Adv. Manuf. Technol."},{"issue":"11","key":"29_CR3","doi-asserted-by":"publisher","first-page":"4938","DOI":"10.3390\/su17114938","volume":"17","author":"J Zhang","year":"2025","unstructured":"Zhang, J. et al.: A review of industrial load flexibility enhancement for demand-response interaction. Sustainability. 17(11), 4938 (2025)","journal-title":"Sustainability"},{"issue":"5","key":"29_CR4","doi-asserted-by":"publisher","first-page":"1144","DOI":"10.3390\/en18051144","volume":"18","author":"O Timur","year":"2025","unstructured":"Timur, O., \u00dcst\u00fcnel, H.Y.: Short-term electric load forecasting for an industrial plant using machine learning-based algorithms. Energies. 18(5), 1144 (2025)","journal-title":"Energies"},{"key":"29_CR5","doi-asserted-by":"publisher","first-page":"9881","DOI":"10.52202\/068431-0718","volume":"35","author":"Y Liu","year":"2022","unstructured":"Liu, Y., Wu, H., Wang, J., Long, M.: Non-stationary transformers: Exploring the stationarity in time series forecasting. Adv. Neural Inf. Proces. Syst. 35, 9881\u20139893 (2022)","journal-title":"Adv. Neural Inf. Proces. Syst."},{"key":"29_CR6","unstructured":"Nie, Y., Nguyen, N.H., Sinthong, P., Kalagnanam, J.: A time series is worth 64 words: Long-term forecasting with transformers. arXiv preprint https:\/\/arxiv.org\/abs\/2211.14730 (2022)."},{"key":"29_CR7","unstructured":"Liu, Y., et al.: iTransformer: Inverted transformers are effective for time series forecasting. arXiv preprint https:\/\/arxiv.org\/abs\/2310.06625 (2023)."},{"key":"29_CR8","first-page":"22419","volume":"34","author":"H Wu","year":"2021","unstructured":"Wu, H., Xu, J., Wang, J., Long, M.: Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting. Adv. Neural Inf. Proces. Syst. 34, 22419\u201322430 (2021)","journal-title":"Adv. Neural Inf. Proces. Syst."},{"key":"29_CR9","unstructured":"Wang, S. et al.: TimeMixer: Decomposable multiscale mixing for time series forecasting. arXiv preprint https:\/\/arxiv.org\/abs\/2405.14616 (2024)."},{"key":"29_CR10","unstructured":"Chen, X., et al.: Rethinking time encoding via learnable transformation functions. arXiv preprint https:\/\/arxiv.org\/abs\/2505.00887 (2025)."},{"issue":"3","key":"29_CR11","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1007\/s10462-024-11044-2","volume":"58","author":"L Su","year":"2025","unstructured":"Su, L., Zuo, X., Li, R., Wang, X., Zhao, H., Huang, B.: A systematic review for transformer-based long-term series forecasting. Artif. Intell. Rev. 58(3), 80 (2025)","journal-title":"Artif. Intell. Rev."},{"key":"29_CR12","unstructured":"Woo, G., Liu, C., Sahoo, D., Kumar, A., Hoi, S.: ETSformer: Exponential smoothing transformers for time-series forecasting. arXiv preprint https:\/\/arxiv.org\/abs\/2202.01381 (2022)."},{"key":"29_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2024.110552","volume":"153","author":"Y Yu","year":"2024","unstructured":"Yu, Y., Ma, R., Ma, Z.: Robformer: A robust decomposition transformer for long-term time series forecasting. Pattern Recogn. 153, 110552 (2024)","journal-title":"Pattern Recogn."},{"key":"29_CR14","unstructured":"Chen, P., et al.: Pathformer: Multi-scale transformers with adaptive pathways for time series forecasting. arXiv preprint https:\/\/arxiv.org\/abs\/2402.05956 (2024)."},{"issue":"1","key":"29_CR15","doi-asserted-by":"publisher","first-page":"62","DOI":"10.3390\/info16010062","volume":"16","author":"Y Jin","year":"2025","unstructured":"Jin, Y., Mao, Y., Chen, G.: DFCNformer: A transformer framework for non-stationary time-series forecasting based on de-stationary Fourier and coefficient network. Information. 16(1), 62 (2025)","journal-title":"Information"},{"key":"29_CR16","unstructured":"Kim, T., Kim, J., Tae, Y., Park, C., Choi, J.-H., Choo, J.: Reversible instance normalization for accurate time-series forecasting against distribution shift. In: International Conference on Learning Representations (2021)."},{"key":"29_CR17","unstructured":"Zhou, T., Ma, Z., Wen, Q., Wang, X., Sun, L., Jin, R.: FEDformer: Frequency enhanced decomposed transformer for long-term series forecasting. In: International Conference on Machine Learning, pp. 27268\u201327286 (2022)."},{"key":"29_CR18","unstructured":"Kazemi, S.M., et al.: Time2Vec: Learning a vector representation of time. arXiv preprint https:\/\/arxiv.org\/abs\/1907.05321 (2019)."},{"key":"29_CR19","unstructured":"Das, A., Kong, W., Leach, A., Mathur, S., Sen, R., Yu, R.: Long-term forecasting with TiDE: Time-series dense encoder. arXiv preprint https:\/\/arxiv.org\/abs\/2304.08424 (2023)."},{"key":"29_CR20","unstructured":"Wang, H., Peng, J., Huang, F., Wang, J., Chen, J., Xiao, Y.: MICN: multi-scale local and global context modeling for long-term series forecasting. In: The Eleventh International Conference on Learning Representations (2023)."},{"issue":"9","key":"29_CR21","first-page":"11121","volume":"37","author":"A Zeng","year":"2023","unstructured":"Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? Proc. AAAI Conf. Artif. Intell. 37(9), 11121\u201311128 (2023)","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"29_CR22","doi-asserted-by":"crossref","unstructured":"Ekambaram, V., Jati, A., Nguyen, N., Sinthong, P., Kalagnanam, J.: TSMixer: Lightweight MLP-mixer model for multivariate time series forecasting. In: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 459\u2013469 (2023).","DOI":"10.1145\/3580305.3599533"},{"key":"29_CR23","doi-asserted-by":"publisher","first-page":"5816","DOI":"10.52202\/068431-0421","volume":"35","author":"M Liu","year":"2022","unstructured":"Liu, M. et al.: SCINet: Time series modeling and forecasting with sample convolution and interaction. Adv. Neural Inf. Proces. Syst. 35, 5816\u20135828 (2022)","journal-title":"Adv. Neural Inf. Proces. Syst."}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-92-3384-7_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T13:25:35Z","timestamp":1783949135000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-92-3384-7_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,14]]},"ISBN":["9789819233830","9789819233847"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-981-92-3384-7_29","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,14]]},"assertion":[{"value":"14 July 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Toronto","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 July 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2026","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":"icic2026a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/2026\/index.htm","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}