{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T18:51:21Z","timestamp":1782499881167,"version":"3.54.5"},"publisher-location":"Singapore","reference-count":29,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819570836","type":"print"},{"value":"9789819570843","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\/978-981-95-7084-3_44","type":"book-chapter","created":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T10:28:56Z","timestamp":1778236136000},"page":"639-654","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SpecMixer: A Frequency-Aware Framework for\u00a0Mitigating Spectral Confusion in\u00a0Multivariate Time Series Forecasting"],"prefix":"10.1007","author":[{"given":"Zihao","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiwei","family":"Qin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dezhi","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Jiao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lulu","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,1]]},"reference":[{"issue":"7970","key":"44_CR1","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1038\/s41586-023-06185-3","volume":"619","author":"K Bi","year":"2023","unstructured":"Bi, K., Xie, L., Zhang, H., Chen, X., Gu, X., Tian, Q.: Accurate medium-range global weather forecasting with 3D neural networks. Nature 619(7970), 533\u2013538 (2023)","journal-title":"Nature"},{"key":"44_CR2","doi-asserted-by":"publisher","unstructured":"Brockwell, P.J., Davis, R.A.: Time Series: Theory and Methods. Springer, Cham (1991). https:\/\/doi.org\/10.1007\/978-1-4419-0320-4","DOI":"10.1007\/978-1-4419-0320-4"},{"issue":"2","key":"44_CR3","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1007\/s10844-022-00713-9","volume":"59","author":"V Cerqueira","year":"2022","unstructured":"Cerqueira, V., Torgo, L., Soares, C.: A case study comparing machine learning with statistical methods for time series forecasting: size matters. J. Intell. Inf. Syst. 59(2), 415\u2013433 (2022)","journal-title":"J. Intell. Inf. Syst."},{"key":"44_CR4","doi-asserted-by":"crossref","unstructured":"Cirstea, R.G., Guo, C., Yang, B., Kieu, T., Dong, X., Pan, S.: Triformer: triangular, variable-specific attentions for long sequence multivariate time series forecasting. In: IJCAI (2022)","DOI":"10.24963\/ijcai.2022\/277"},{"issue":"1","key":"44_CR5","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1016\/S0306-2619(03)00096-5","volume":"77","author":"JC Cuaresma","year":"2004","unstructured":"Cuaresma, J.C., Hlouskova, J., Kossmeier, S., Obersteiner, M.: Forecasting electricity spot-prices using linear univariate time-series models. Appl. Energy 77(1), 87\u2013106 (2004)","journal-title":"Appl. Energy"},{"key":"44_CR6","unstructured":"Das, A., Kong, W., Leach, A., Mathur, S., Sen, R., Yu, R.: Long-term forecasting with tide: time-series dense encoder"},{"key":"44_CR7","doi-asserted-by":"crossref","unstructured":"Du, Y., et al.: Adarnn: adaptive learning and forecasting of time series. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp. 402\u2013411 (2021)","DOI":"10.1145\/3459637.3482315"},{"key":"44_CR8","doi-asserted-by":"crossref","unstructured":"El\u00a0Zaar, A., Mansouri, A., Benaya, N., Bakir, T., El\u00a0Allati, A.: Hybrid transformer-cnn architecture for multivariate time series forecasting: integrating attention mechanisms with convolutional feature extraction. J. Intell. Inf. Syst. 1\u201332 (2025)","DOI":"10.1007\/s10844-025-00937-5"},{"key":"44_CR9","unstructured":"Eldele, E., Ragab, M., Chen, Z., Wu, M., Li, X.: Tslanet: rethinking transformers for time series representation learning. arXiv preprint arXiv:2404.08472 (2024)"},{"key":"44_CR10","doi-asserted-by":"crossref","unstructured":"Gao, Y., Su, R., Ben, X., Chen, L.: Est transformer: enhanced spatiotemporal representation learning for time series anomaly detection. J. Intell. Inf. Syst. 1\u201323 (2025)","DOI":"10.1007\/s10844-025-00918-8"},{"key":"44_CR11","doi-asserted-by":"publisher","first-page":"2542","DOI":"10.1109\/LSP.2022.3228131","volume":"29","author":"E Ko\u00e7","year":"2022","unstructured":"Ko\u00e7, E., Ko\u00e7, A.: Fractional fourier transform in time series prediction. IEEE Signal Process. Lett. 29, 2542\u20132546 (2022)","journal-title":"IEEE Signal Process. Lett."},{"key":"44_CR12","unstructured":"Li, Z., Qi, S., Li, Y., Xu, Z.: Revisiting long-term time series forecasting: an investigation on linear mapping. ArXiv abs\/2305.10721 (2023)"},{"key":"44_CR13","doi-asserted-by":"crossref","unstructured":"Liu, P., et al.: Wftnet: exploiting global and local periodicity in long-term time series forecasting. In: ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5960\u20135964. IEEE (2024)","DOI":"10.1109\/ICASSP48485.2024.10446883"},{"key":"44_CR14","unstructured":"Liu, S., et al.: Pyraformer: low-complexity pyramidal attention for long-range time series modeling and forecasting. In: International Conference on Learning Representations (2022)"},{"key":"44_CR15","unstructured":"Liu, Y., et al.: Itransformer: inverted transformers are effective for time series forecasting. arXiv preprint arXiv:2310.06625 (2023)"},{"key":"44_CR16","doi-asserted-by":"crossref","unstructured":"Liu, Y., Wu, H., Wang, J., Long, M.: Non-stationary transformers: exploring the stationarity in time series forecasting. Adv. Neural Inf. Process. Syst. 35, 9881\u20139893 (2022)","DOI":"10.52202\/068431-0718"},{"key":"44_CR17","unstructured":"Nie, Y., Nguyen, H.N., Sinthong, P., Kalagnanam, J.: A time series is worth 64 words: long-term forecasting with transformers. In: International Conference on Learning Representations (2023)"},{"key":"44_CR18","doi-asserted-by":"crossref","unstructured":"Piao, X., Chen, Z., Murayama, T., Matsubara, Y., Sakurai, Y.: Fredformer: frequency debiased transformer for time series forecasting. In: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. KDD \u201924 (2024)","DOI":"10.1145\/3637528.3671928"},{"key":"44_CR19","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.matcom.2016.03.003","volume":"126","author":"JR Thompson","year":"2016","unstructured":"Thompson, J.R., Wilson, J.R.: Multifractal detrended fluctuation analysis: practical applications to financial time series. Math. Comput. Simul. 126, 63\u201388 (2016)","journal-title":"Math. Comput. Simul."},{"key":"44_CR20","unstructured":"Wang, H., et al.: Fredf: learning to forecast in frequency domain. arXiv preprint arXiv:2402.02399 (2024)"},{"key":"44_CR21","unstructured":"Wu, H., Hu, T., Liu, Y., Zhou, H., Wang, J., Long, M.: Timesnet: temporal 2d-variation modeling for general time series analysis. In: International Conference on Learning Representations (2023)"},{"key":"44_CR22","unstructured":"Wu, H., Xu, J., Wang, J., Long, M.: Autoformer: decomposition transformers with auto-correlation for long-term series forecasting. Adv. Neural Inf. Process. Syst. 34, 22419\u201322430 (2021)"},{"key":"44_CR23","doi-asserted-by":"crossref","unstructured":"Yang, R., Cao, L., Yang, J., et\u00a0al.: Rethinking fourier transform from a basis functions perspective for long-term time series forecasting. Adv. Neural Inf. Process. Syst. 37, 8515\u20138540 (2024)","DOI":"10.52202\/079017-0272"},{"key":"44_CR24","unstructured":"Ye, H., et al.: Atfnet: adaptive time-frequency ensembled network for long-term time series forecasting. arXiv preprint arXiv:2404.05192 (2024)"},{"key":"44_CR25","doi-asserted-by":"crossref","unstructured":"Yi, K., et al.: Frequency-domain mlps are more effective learners in time series forecasting. Adv. Neural Inf. Process. Syst. 36, 76656\u201376679 (2023)","DOI":"10.52202\/075280-3349"},{"key":"44_CR26","doi-asserted-by":"crossref","unstructured":"Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? (2023)","DOI":"10.1609\/aaai.v37i9.26317"},{"key":"44_CR27","unstructured":"Zhang, Y., Yan, J.: Crossformer: transformer utilizing cross-dimension dependency for multivariate time series forecasting. In: International Conference on Learning Representations (2023)"},{"key":"44_CR28","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. PMLR (2022)"},{"issue":"4","key":"44_CR29","doi-asserted-by":"publisher","first-page":"951","DOI":"10.3390\/sym15040951","volume":"15","author":"Q Zhu","year":"2023","unstructured":"Zhu, Q., Han, J., Chai, K., Zhao, C.: Time series analysis based on informer algorithms: a survey. Symmetry 15(4), 951 (2023)","journal-title":"Symmetry"}],"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\/978-981-95-7084-3_44","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T18:24:25Z","timestamp":1782498265000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-7084-3_44"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819570836","9789819570843"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-7084-3_44","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":"1 May 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"}}]}}