{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T22:12:20Z","timestamp":1763417540189,"version":"3.45.0"},"publisher-location":"Singapore","reference-count":40,"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_20","type":"book-chapter","created":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T22:07:42Z","timestamp":1763417262000},"page":"260-274","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["PAformer: Transformer with\u00a0Learnable Period Detection and\u00a0Periodic Attention for\u00a0Multivariate Time Series"],"prefix":"10.1007","author":[{"given":"Meng","family":"Wan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huan","family":"Hao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuxuan","family":"Bi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jue","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Su","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Shi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xueyan","family":"Wei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yangang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shulong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Helian","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,11,18]]},"reference":[{"key":"20_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108218","volume":"121","author":"D Cheng","year":"2022","unstructured":"Cheng, D., Yang, F., Xiang, S., Liu, J.: Financial time series forecasting with multi-modality graph neural network. Pattern Recogn. 121, 108218 (2022)","journal-title":"Pattern Recogn."},{"key":"20_CR2","doi-asserted-by":"publisher","first-page":"4","DOI":"10.3389\/fdata.2020.00004","volume":"3","author":"S Kaushik","year":"2020","unstructured":"Kaushik, S., et al.: AI in healthcare: time-series forecasting using statistical, neural, and ensemble architectures. Front. Big Data 3, 4 (2020)","journal-title":"Front. Big Data"},{"issue":"12","key":"20_CR3","doi-asserted-by":"publisher","first-page":"9179","DOI":"10.1109\/JIOT.2021.3100509","volume":"9","author":"Z Chen","year":"2021","unstructured":"Chen, Z., Chen, D., Zhang, X., Yuan, Z., Cheng, X.: Learning graph structures with transformer for multivariate time-series anomaly detection in iot. IEEE Internet Things J. 9(12), 9179\u20139189 (2021)","journal-title":"IEEE Internet Things J."},{"key":"20_CR4","unstructured":"Gamboa, J.C.B.: Deep learning for time-series analysis. arXiv preprint arXiv:1701.01887 (2017)"},{"key":"20_CR5","unstructured":"Nie, Y., Nguyen, N.H., Sinthong, P., Kalagnanam, J.: A time series is worth 64 words: long-term forecasting with transformers. In: The Eleventh International Conference on Learning Representations"},{"key":"20_CR6","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.\u00a035, pp. 11106\u201311115 (2021)","DOI":"10.1609\/aaai.v35i12.17325"},{"key":"20_CR7","doi-asserted-by":"crossref","unstructured":"Jin, C., et al.: Hetgat: a heterogeneous graph attention network for freeway traffic speed prediction. J. Ambient Intell. Humanized Comput. 1\u201312 (2021)","DOI":"10.1007\/s12652-020-02807-0"},{"key":"20_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.118556","volume":"211","author":"Y Dang","year":"2023","unstructured":"Dang, Y., Zhang, Y., Wang, J.: A novel multivariate grey model for forecasting periodic oscillation time series. Expert Syst. Appl. 211, 118556 (2023)","journal-title":"Expert Syst. Appl."},{"key":"20_CR9","doi-asserted-by":"crossref","unstructured":"Cai, W., Liang, Y., Liu, X., Feng, J., Wu, Y.: Msgnet: learning multi-scale inter-series correlations for multivariate time series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a038, pp. 11141\u201311149 (2024)","DOI":"10.1609\/aaai.v38i10.28991"},{"key":"20_CR10","unstructured":"Wu, H., Hu, T., Liu, Y., Zhou, H., Wang, J., Long, M.: Timesnet: temporal 2d-variation modeling for general time series analysis. arXiv preprint arXiv:2210.02186 (2022)"},{"key":"20_CR11","unstructured":"Zhou, Z., Lyu, G., Huang, Y., Wang, Z., Jia, Z., Yang, Z.: Sdformer: transformer with spectral filter and dynamic attention for multivariate time series long-term forecasting (2024)"},{"issue":"1","key":"20_CR12","doi-asserted-by":"publisher","first-page":"99","DOI":"10.5194\/npg-31-99-2024","volume":"31","author":"W Li","year":"2024","unstructured":"Li, W., Guo, J.: Extraction of periodic signals in global navigation satellite system (GNSS) vertical coordinate time series using the adaptive ensemble empirical modal decomposition method. Nonlinear Process. Geophys. 31(1), 99\u2013113 (2024)","journal-title":"Nonlinear Process. Geophys."},{"key":"20_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2021.108616","volume":"168","author":"Y Ding","year":"2022","unstructured":"Ding, Y., Jia, M., Miao, Q., Cao, Y.: A novel time-frequency transformer based on self-attention mechanism and its application in fault diagnosis of rolling bearings. Mech. Syst. Signal Process. 168, 108616 (2022)","journal-title":"Mech. Syst. Signal Process."},{"key":"20_CR14","doi-asserted-by":"crossref","unstructured":"Song, Z., Yu, J., Chen, Y.P.P., Yang, W.: Transformer tracking with cyclic shifting window attention. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8791\u20138800 (2022)","DOI":"10.1109\/CVPR52688.2022.00859"},{"key":"20_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2022.108701","volume":"226","author":"Y Chang","year":"2022","unstructured":"Chang, Y., Li, F., Chen, J., Liu, Y., Li, Z.: Efficient temporal flow transformer accompanied with multi-head probsparse self-attention mechanism for remaining useful life prognostics. Reliabil. Eng. Syst. Saf. 226, 108701 (2022)","journal-title":"Reliabil. Eng. Syst. Saf."},{"issue":"1","key":"20_CR16","doi-asserted-by":"publisher","first-page":"120","DOI":"10.3390\/vehicles6010005","volume":"6","author":"Z Yang","year":"2024","unstructured":"Yang, Z., Zhang, Q., Chang, W., Xiao, P., Li, M.: Egformer: an enhanced transformer model with efficient attention mechanism for traffic flow forecasting. Vehicles 6(1), 120\u2013139 (2024)","journal-title":"Vehicles"},{"issue":"2","key":"20_CR17","doi-asserted-by":"publisher","first-page":"407","DOI":"10.3390\/electronics13020407","volume":"13","author":"M Irani Azad","year":"2024","unstructured":"Irani Azad, M., Rajabi, R., Estebsari, A.: Nonintrusive load monitoring (NILM) using a deep learning model with a transformer-based attention mechanism and temporal pooling. Electronics 13(2), 407 (2024)","journal-title":"Electronics"},{"issue":"8","key":"20_CR18","doi-asserted-by":"publisher","first-page":"6037","DOI":"10.1007\/s10462-022-10148-x","volume":"55","author":"A de Santana Correia","year":"2022","unstructured":"de Santana Correia, A., Colombini, E.L.: Attention, please! a survey of neural attention models in deep learning. Artif. Intell. Rev. 55(8), 6037\u20136124 (2022)","journal-title":"Artif. Intell. Rev."},{"key":"20_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.enggeo.2024.107446","volume":"331","author":"Q Ge","year":"2024","unstructured":"Ge, Q., Li, J., Wang, X., Deng, Y., Zhang, K., Sun, H.: Litetransnet: an interpretable approach for landslide displacement prediction using transformer model with attention mechanism. Eng. Geol. 331, 107446 (2024)","journal-title":"Eng. Geol."},{"issue":"3","key":"20_CR20","volume":"2","author":"SG Wawale","year":"2022","unstructured":"Wawale, S.G., Bisht, A., Vyas, S., Narawish, C., Ray, S.: An overview: modeling and forecasting of time series data using different techniques in reference to human stress. Neurosci. Inf. 2(3), 100052 (2022)","journal-title":"Neurosci. Inf."},{"issue":"1","key":"20_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/15567036.2019.1649756","volume":"44","author":"A Takilalte","year":"2022","unstructured":"Takilalte, A., Harrouni, S., Mora, J.: Forecasting global solar irradiance for various resolutions using time series models-case study: Algeria. Energy Sources Part A: Rec. Utilizat. Environ. Effects 44(1), 1\u201320 (2022)","journal-title":"Energy Sources Part A: Rec. Utilizat. Environ. Effects"},{"key":"20_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.cageo.2022.105126","volume":"164","author":"Y Ning","year":"2022","unstructured":"Ning, Y., Kazemi, H., Tahmasebi, P.: A comparative machine learning study for time series oil production forecasting: arima, lstm, and prophet. Comput. Geosci. 164, 105126 (2022)","journal-title":"Comput. Geosci."},{"key":"20_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.120203","volume":"227","author":"Q Ren","year":"2023","unstructured":"Ren, Q., Li, Y., Liu, Y.: Transformer-enhanced periodic temporal convolution network for long short-term traffic flow forecasting. Expert Syst. Appl. 227, 120203 (2023)","journal-title":"Expert Syst. Appl."},{"key":"20_CR24","unstructured":"Zhao, X., Wang, N.: Enformer: encoder-based sparse periodic self-attention time-series forecasting. IEEE Access (2023)"},{"key":"20_CR25","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1016\/j.neucom.2023.02.061","volume":"534","author":"X Liao","year":"2023","unstructured":"Liao, X., Liu, Z., Zheng, X., Ping, Z., He, X.: Wind power prediction based on periodic characteristic decomposition and multi-layer attention network. Neurocomputing 534, 119\u2013132 (2023)","journal-title":"Neurocomputing"},{"key":"20_CR26","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. Process. Syst. 34, 22419\u201322430 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"20_CR27","unstructured":"Dai, T., et al.: Periodicity decoupling framework for long-term series forecasting. In: The Twelfth International Conference on Learning Representations (2024)"},{"key":"20_CR28","unstructured":"Lin, S., Lin, W., Wu, W., Chen, H., Yang, J.: Sparsetsf: modeling long-term time series forecastinfg with 1k parameters. arXiv preprint arXiv:2405.00946 (2024)"},{"issue":"4","key":"20_CR29","doi-asserted-by":"publisher","first-page":"4958","DOI":"10.1109\/JSYST.2020.3022640","volume":"15","author":"H Kim","year":"2020","unstructured":"Kim, H., Yun, U., Vo, B., Lin, J.C.W., Pedrycz, W.: Periodicity-oriented data analytics on time-series data for intelligence system. IEEE Syst. J. 15(4), 4958\u20134969 (2020)","journal-title":"IEEE Syst. J."},{"key":"20_CR30","doi-asserted-by":"crossref","unstructured":"Wen, Q., He, K., Sun, L., Zhang, Y., Ke, M., Xu, H.: Robustperiod: robust time-frequency mining for multiple periodicity detection. In: Proceedings of the 2021 International Conference on Management of Data, pp. 2328\u20132337 (2021)","DOI":"10.1145\/3448016.3452779"},{"key":"20_CR31","unstructured":"Liu, S., et al.: Pyraformer: low-complexity pyramidal attention for long-range time series modeling and forecasting. In: International Conference on Learning Representations (2021)"},{"key":"20_CR32","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)"},{"key":"20_CR33","unstructured":"Chen, P., et al.: Multi-scale transformers with adaptive pathways for time series forecasting. In: International Conference on Learning Representations (2024)"},{"key":"20_CR34","unstructured":"Liu, Y., et al.: itransformer: inverted transformers are effective for time series forecasting. In: The Twelfth International Conference on Learning Representations (2023)"},{"issue":"1","key":"20_CR35","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0227222","volume":"15","author":"J Qiu","year":"2020","unstructured":"Qiu, J., Wang, B., Zhou, C.: Forecasting stock prices with long-short term memory neural network based on attention mechanism. PLoS ONE 15(1), e0227222 (2020)","journal-title":"PLoS ONE"},{"key":"20_CR36","doi-asserted-by":"publisher","first-page":"472","DOI":"10.1016\/j.ins.2022.07.178","volume":"610","author":"R Chen","year":"2022","unstructured":"Chen, R., Yan, X., Wang, S., Xiao, G.: Da-net: dual-attention network for multivariate time series classification. Inf. Sci. 610, 472\u2013487 (2022)","journal-title":"Inf. Sci."},{"key":"20_CR37","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1016\/j.neucom.2022.06.014","volume":"501","author":"E Fu","year":"2022","unstructured":"Fu, E., Zhang, Y., Yang, F., Wang, S.: Temporal self-attention-based conv-lstm network for multivariate time series prediction. Neurocomputing 501, 162\u2013173 (2022)","journal-title":"Neurocomputing"},{"key":"20_CR38","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1016\/j.ins.2023.01.095","volume":"628","author":"Y Dong","year":"2023","unstructured":"Dong, Y., Xiao, L., Wang, J., Wang, J.: A time series attention mechanism based model for tourism demand forecasting. Inf. Sci. 628, 269\u2013290 (2023)","journal-title":"Inf. Sci."},{"key":"20_CR39","doi-asserted-by":"crossref","unstructured":"Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a037, pp. 11121\u201311128 (2023)","DOI":"10.1609\/aaai.v37i9.26317"},{"key":"20_CR40","unstructured":"Das, A., Kong, W., Leach, A., Mathur, S.K., Sen, R., Yu, R.: Long-term forecasting with tide: time-series dense encoder. Trans. Mach. Learn. Res. (2023)"}],"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_20","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_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,18]]},"ISBN":["9789819530519","9789819530526"],"references-count":40,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-3052-6_20","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"}}]}}