{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,25]],"date-time":"2025-06-25T10:40:02Z","timestamp":1750848002310,"version":"3.41.0"},"publisher-location":"Singapore","reference-count":17,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819687305","type":"print"},{"value":"9789819687312","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-981-96-8731-2_22","type":"book-chapter","created":{"date-parts":[[2025,6,25]],"date-time":"2025-06-25T09:59:34Z","timestamp":1750845574000},"page":"225-234","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Hybrid EEG Forecasting Model with\u00a0Rolling Mapping-Partial Decomposition and\u00a0LSTM"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7929-2509","authenticated-orcid":false,"given":"Chenhao","family":"Wu","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9756-7326","authenticated-orcid":false,"given":"Xiangjun","family":"Cai","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0651-0071","authenticated-orcid":false,"given":"Sheng","family":"Zhou","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0613-0298","authenticated-orcid":false,"given":"Jiang","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,6,21]]},"reference":[{"issue":"4","key":"22_CR1","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1016\/j.tins.2017.02.004","volume":"40","author":"MX Cohen","year":"2017","unstructured":"Cohen, M.X.: Where does EEG come from and what does it mean? Trends Neurosci. 40(4), 208\u2013218 (2017)","journal-title":"Trends Neurosci."},{"key":"22_CR2","volume-title":"EEG Signal Processing","author":"S Sanei","year":"2013","unstructured":"Sanei, S., Chambers, J.A.: EEG Signal Processing. Wiley, Hoboken (2013)"},{"issue":"425","key":"22_CR3","first-page":"208","volume":"89","author":"T Ter\u00e4svirta","year":"1994","unstructured":"Ter\u00e4svirta, T.: Specification, estimation, and evaluation of smooth transition autoregressive models. J. Am. Stat. Assoc. 89(425), 208\u2013218 (1994)","journal-title":"J. Am. Stat. Assoc."},{"key":"22_CR4","doi-asserted-by":"crossref","unstructured":"Shumway, R.H., Stoffer, D.S., Shumway, R.H., Stoffer, D.S.: ARIMA Models. Time Series Analysis and its Applications: With R Examples, pp. 75\u2013163 (2017)","DOI":"10.1007\/978-3-319-52452-8_3"},{"issue":"2.5","key":"22_CR5","first-page":"3","volume":"37","author":"V Jakkula","year":"2006","unstructured":"Jakkula, V.: Tutorial on support vector machine (SVM). School EECS Washington State Univ. 37(2.5), 3 (2006)","journal-title":"School EECS Washington State Univ."},{"issue":"1","key":"22_CR6","doi-asserted-by":"publisher","first-page":"31","DOI":"10.17849\/insm-47-01-31-39.1","volume":"47","author":"SJ Rigatti","year":"2017","unstructured":"Rigatti, S.J.: Random forest. J. Insur. Med. 47(1), 31\u201339 (2017)","journal-title":"J. Insur. Med."},{"issue":"7","key":"22_CR7","doi-asserted-by":"publisher","first-page":"1235","DOI":"10.1162\/neco_a_01199","volume":"31","author":"Y Yu","year":"2019","unstructured":"Yu, Y., Si, X., Hu, C., Zhang, J.: A review of recurrent neural networks: LSTM cells and network architectures. Neural Comput. 31(7), 1235\u20131270 (2019)","journal-title":"Neural Comput."},{"key":"22_CR8","doi-asserted-by":"crossref","unstructured":"Dey, R., Salem, F.M.: Gate-variants of gated recurrent unit (GRU) neural networks. In: 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS), pp. 1597\u20131600. IEEE (2017)","DOI":"10.1109\/MWSCAS.2017.8053243"},{"issue":"3","key":"22_CR9","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1109\/81.222795","volume":"40","author":"LO Chua","year":"1993","unstructured":"Chua, L.O., Roska, T.: The CNN paradigm. IEEE Trans. Circuits Syst. I: Fundam. Theory Appl. 40(3), 147\u2013156 (1993)","journal-title":"IEEE Trans. Circuits Syst. I: Fundam. Theory Appl."},{"issue":"10","key":"22_CR10","doi-asserted-by":"publisher","first-page":"102256","DOI":"10.1016\/j.apr.2024.102256","volume":"15","author":"Q Yu","year":"2024","unstructured":"Yu, Q., Yuan, H.W., Liu, Z.L., Xu, G.M.: Spatial weighting EMD-LSTM based approach for short-term PM2.5 prediction research. Atmos. Pollut. Res. 15(10), 102256 (2024)","journal-title":"Atmos. Pollut. Res."},{"key":"22_CR11","doi-asserted-by":"publisher","first-page":"132766","DOI":"10.1016\/j.energy.2024.132766","volume":"307","author":"S Cui","year":"2024","unstructured":"Cui, S., Lyu, S., Ma, Y., Wang, K.: Improved informer PV power short-term prediction model based on weather typing and AHA-VMD-MPE. Energy 307, 132766 (2024)","journal-title":"Energy"},{"key":"22_CR12","doi-asserted-by":"publisher","first-page":"111157","DOI":"10.1016\/j.knosys.2023.111157","volume":"283","author":"X Cai","year":"2024","unstructured":"Cai, X., Li, D.: M-EDEM: a MNN-based empirical decomposition ensemble method for improved time series forecasting. Knowl.-Based Syst. 283, 111157 (2024)","journal-title":"Knowl.-Based Syst."},{"key":"22_CR13","doi-asserted-by":"crossref","unstructured":"Cai, X., Li, D., Zhang, J., Wu, Z.: MA-EMD: aligned empirical decomposition for multivariate time-series forecasting. Expert Syst. Appl. 126080 (2024)","DOI":"10.1016\/j.eswa.2024.126080"},{"key":"22_CR14","doi-asserted-by":"publisher","first-page":"119869","DOI":"10.1016\/j.energy.2021.119869","volume":"222","author":"L Yu","year":"2021","unstructured":"Yu, L., Ma, Y., Ma, M.: An effective rolling decomposition-ensemble model for gasoline consumption forecasting. Energy 222, 119869 (2021)","journal-title":"Energy"},{"issue":"9","key":"22_CR15","doi-asserted-by":"publisher","first-page":"1185","DOI":"10.1109\/10.867928","volume":"47","author":"B Kemp","year":"2000","unstructured":"Kemp, B., Zwinderman, A.H., Tuk, B., Kamphuisen, H.A., Oberye, J.J.: Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the EEG. IEEE Trans. Biomed. Eng. 47(9), 1185\u20131194 (2000)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"22_CR16","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1016\/j.atmosenv.2019.04.011","volume":"211","author":"J Xu","year":"2019","unstructured":"Xu, J., et al.: An advanced spatio-temporal model for particulate matter and gaseous pollutants in Beijing, China. Atmos. Environ. 211, 120\u2013127 (2019)","journal-title":"Atmos. Environ."},{"key":"22_CR17","doi-asserted-by":"publisher","first-page":"122091","DOI":"10.1016\/j.eswa.2023.122091","volume":"238","author":"D Wen","year":"2024","unstructured":"Wen, D., Zhao, T., Fang, L., Zhang, C., Li, X.: MWDINet: a multilevel wavelet decomposition interaction network for stock price prediction. Expert Syst. Appl. 238, 122091 (2024)","journal-title":"Expert Syst. Appl."}],"container-title":["Lecture Notes in Computer Science","Wireless Artificial Intelligent Computing Systems and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-8731-2_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,25]],"date-time":"2025-06-25T09:59:37Z","timestamp":1750845577000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-8731-2_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819687305","9789819687312"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-8731-2_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"21 June 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"WASA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Wireless Artificial Intelligent Computing Systems and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tokyo","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","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":"24 June 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 June 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"wasa2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/wasa-conference.org\/WASA2025\/index.html#","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}