{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T02:04:22Z","timestamp":1743127462988,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":22,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819756650"},{"type":"electronic","value":"9789819756667"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-981-97-5666-7_3","type":"book-chapter","created":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T20:37:45Z","timestamp":1722544665000},"page":"27-40","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["IFTNet: Interpolation Frequency- and Time-Domain Network for Long-Term Time Series Forecasting"],"prefix":"10.1007","author":[{"given":"Xuelin","family":"Cheng","sequence":"first","affiliation":[]},{"given":"Haozheng","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Botao","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Xu","family":"Zou","sequence":"additional","affiliation":[]},{"given":"Xince","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Runjie","family":"Zhao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,8,1]]},"reference":[{"unstructured":"Bai, S., Kolter, J.Z., Koltun, V.: An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271 (2018)","key":"3_CR1"},{"doi-asserted-by":"crossref","unstructured":"Brigham, E.O., et al.: The fast Fourier transform. IEEE Spectr. 4(12), 63\u201370 (1967)","key":"3_CR2","DOI":"10.1109\/MSPEC.1967.5217220"},{"doi-asserted-by":"crossref","unstructured":"Challu, C., et al.: NHITS: neural hierarchical interpolation for time series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 6989\u20136997 (2023)","key":"3_CR3","DOI":"10.1609\/aaai.v37i6.25854"},{"doi-asserted-by":"crossref","unstructured":"Chen, Y., Chen, X., Xu, A., Sun, Q., Peng, X.: A hybrid CNN-transformer model for ozone concentration prediction. Air Qual. Atmos. Health 15(9), 1533\u20131546 (2022)","key":"3_CR4","DOI":"10.1007\/s11869-022-01197-w"},{"unstructured":"Das, A., Kong, W., Leach, A., Sen, R., Yu, R.: Long-term forecasting with tide: time-series dense encoder. arXiv preprint arXiv:2304.08424 (2023)","key":"3_CR5"},{"issue":"4","key":"3_CR6","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1007\/BF00344251","volume":"36","author":"K Fukushima","year":"1980","unstructured":"Fukushima, K.: Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cybern. 36(4), 193\u2013202 (1980)","journal-title":"Biol. Cybern."},{"unstructured":"Gu, A., et al.: Efficiently modeling long sequences with structured state spaces. In: The 10th International Conference on Learning Representations (2022)","key":"3_CR7"},{"issue":"4","key":"3_CR8","doi-asserted-by":"publisher","first-page":"733","DOI":"10.1007\/s41095-023-0364-2","volume":"9","author":"MH Guo","year":"2023","unstructured":"Guo, M.H., Lu, C.Z., Liu, Z.N., Cheng, M.M., Hu, S.M.: Visual attention network. Comput. Vis. Media 9(4), 733\u2013752 (2023)","journal-title":"Comput. Vis. Media"},{"issue":"8","key":"3_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."},{"doi-asserted-by":"crossref","unstructured":"Lai, G., Chang, W.C., Yang, Y., Liu, H.: Modeling long-and short-term temporal patterns with deep neural networks. In: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 95\u2013104 (2018)","key":"3_CR10","DOI":"10.1145\/3209978.3210006"},{"unstructured":"Liu, M., et al.: SCINet: time series modeling and forecasting with sample convolution and interaction. In: Advances in Neural Information Processing Systems, vol. 35, pp. 5816\u20135828 (2022)","key":"3_CR11"},{"unstructured":"Liu, Y., et al.: Non-stationary transformers: exploring the stationarity in time series forecasting. In: Advances in Neural Information Processing Systems, vol. 35, pp. 9881\u20139893 (2022)","key":"3_CR12"},{"unstructured":"Nie, Y., et al.: A time series is worth 64 words: long-term forecasting with transformers. In: The Eleventh International Conference on Learning Representations (2023)","key":"3_CR13"},{"doi-asserted-by":"crossref","unstructured":"Nosratabadi, et al.: Data science in economics: comprehensive review of advanced machine learning and deep learning methods. Mathematics 8(10), 1799 (2020)","key":"3_CR14","DOI":"10.3390\/math8101799"},{"doi-asserted-by":"crossref","unstructured":"Papadimitriou, S., Yu, P.: Optimal multi-scale patterns in time series streams. In: Proceedings of the 2006 ACM SIGMOD International Conference on Management of Data, pp. 647\u2013658 (2006)","key":"3_CR15","DOI":"10.1145\/1142473.1142545"},{"unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)","key":"3_CR16"},{"unstructured":"Wu, H., et al.: TimesNet: temporal 2D-variation modeling for general time series analysis. In: The Eleventh International Conference on Learning Representations (2023)","key":"3_CR17"},{"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 (2021)","key":"3_CR18"},{"doi-asserted-by":"crossref","unstructured":"Zeng, A., et al.: Are transformers effective for time series forecasting? In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 11121\u201311128 (2023)","key":"3_CR19","DOI":"10.1609\/aaai.v37i9.26317"},{"unstructured":"Zhang, Y., Yan, J.: Crossformer: transformer utilizing cross-dimension dependency for multivariate time series forecasting. In: The Eleventh International Conference on Learning Representations (2022)","key":"3_CR20"},{"doi-asserted-by":"crossref","unstructured":"Zhou, H., et al.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106\u201311115 (2021)","key":"3_CR21","DOI":"10.1609\/aaai.v35i12.17325"},{"unstructured":"Zhou, T., et al.: FEDformer: frequency enhanced decomposed transformer for long-term series forecasting. In: International Conference on Machine Learning, pp. 27268\u201327286. PMLR (2022)","key":"3_CR22"}],"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\/978-981-97-5666-7_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T20:39:27Z","timestamp":1722544767000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-5666-7_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819756650","9789819756667"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-5666-7_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"1 August 2024","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":"Tianjin","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":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 August 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 August 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/2024\/index.htm","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}