{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T17:23:06Z","timestamp":1777569786513,"version":"3.51.4"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031262838","type":"print"},{"value":"9783031262845","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-26284-5_27","type":"book-chapter","created":{"date-parts":[[2023,2,22]],"date-time":"2023-02-22T08:02:59Z","timestamp":1677052979000},"page":"442-457","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Flare Transformer: Solar Flare Prediction Using Magnetograms and\u00a0Sunspot Physical Features"],"prefix":"10.1007","author":[{"given":"Kanta","family":"Kaneda","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuiga","family":"Wada","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tsumugi","family":"Iida","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Naoto","family":"Nishizuka","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Y\u00fbki","family":"Kubo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Komei","family":"Sugiura","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,2,23]]},"reference":[{"issue":"1","key":"27_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41597-020-0548-x","volume":"7","author":"RA Angryk","year":"2020","unstructured":"Angryk, R.A., et al.: Multivariate time series dataset for space weather data analytics. Sci. Data 7(1), 1\u201313 (2020)","journal-title":"Sci. Data"},{"issue":"2","key":"27_CR2","doi-asserted-by":"publisher","first-page":"98","DOI":"10.3847\/1538-4357\/ab9c29","volume":"898","author":"S Bhattacharjee","year":"2020","unstructured":"Bhattacharjee, S., Alshehhi, R., Dhuri, D., et al.: Supervised convolutional neural networks for classification of flaring and nonflaring active regions using line-of-sight magnetograms. Astrophys. J. 898(2), 98 (2020)","journal-title":"Astrophys. J."},{"key":"27_CR3","volume-title":"Time Series Analysis: Forecasting and Control","author":"G Box","year":"2015","unstructured":"Box, G., Jenkins, G., Reinsel, G., Ljung, G.: Time Series Analysis: Forecasting and Control. Wiley, Hoboken (2015)"},{"issue":"3","key":"27_CR4","doi-asserted-by":"publisher","first-page":"3332","DOI":"10.1093\/mnras\/staa1257","volume":"495","author":"T Cinto","year":"2020","unstructured":"Cinto, T., Gradvohl, S., Coelho, P., Silva, A.: A framework for designing and evaluating solar flare forecasting systems. Mon. Not. R. Astron. Soc. 495(3), 3332\u20133349 (2020)","journal-title":"Mon. Not. R. Astron. Soc."},{"issue":"1","key":"27_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2379776.2379788","volume":"45","author":"P Esling","year":"2012","unstructured":"Esling, P., Agon, C.: Time-series data mining. ACM Comput. Surv. 45(1), 1\u201334 (2012)","journal-title":"ACM Comput. Surv."},{"issue":"2","key":"27_CR6","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1175\/1520-0493(1992)120<0361:ESSFCF>2.0.CO;2","volume":"120","author":"L Gandin","year":"1992","unstructured":"Gandin, L., Murphy, A.: Equitable skill scores for categorical forecasts. Mon. Weather Rev. 120(2), 361\u2013370 (1992)","journal-title":"Mon. Weather Rev."},{"key":"27_CR7","doi-asserted-by":"crossref","unstructured":"Georgoulis, M., Bloomfield, S., et al.: The flare likelihood and region eruption forecasting (FLARECAST) project: flare forecasting in the big data & machine learning era. J. Space Weather Space Clim. 11, A39 (2021)","DOI":"10.1051\/swsc\/2021023"},{"key":"27_CR8","doi-asserted-by":"crossref","unstructured":"Huang, X., Wang, H., Xu, L., Liu, J., Li, R., Dai, X.: Deep learning based solar flare forecasting model. I. Results for line-of-sight magnetograms. Astrophys. J. 856(1), 7 (2018)","DOI":"10.3847\/1538-4357\/aaae00"},{"key":"27_CR9","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-71918-2","volume-title":"Forecasting with Exponential Smoothing: The State Space Approach","author":"R Hyndman","year":"2008","unstructured":"Hyndman, R., Koehler, A., et al.: Forecasting with Exponential Smoothing: The State Space Approach. Springer, Heidelberg (2008). https:\/\/doi.org\/10.1007\/978-3-540-71918-2"},{"key":"27_CR10","doi-asserted-by":"crossref","unstructured":"Kubo, Y., Den, M., Ishii, M.: Verification of operational solar flare forecast: case of regional warning center Japan. J. Space Weather Space Clim. 7, A20 (2017)","DOI":"10.1051\/swsc\/2017018"},{"issue":"6503","key":"27_CR11","doi-asserted-by":"publisher","first-page":"587","DOI":"10.1126\/science.aaz2511","volume":"369","author":"K Kusano","year":"2020","unstructured":"Kusano, K., Iju, T., Bamba, Y., Inoue, S.: A physics-based method that can predict imminent large solar flares. Science 369(6503), 587\u2013591 (2020)","journal-title":"Science"},{"key":"27_CR12","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1007\/978-1-4614-3673-7_3","volume-title":"The Solar Dynamics Observatory","author":"J Lemen","year":"2011","unstructured":"Lemen, J., et al.: The atmospheric imaging assembly (AIA) on the solar dynamics observatory (SDO). In: Chamberlin, P., Pesnell, W.D., Thompson, B. (eds.) The Solar Dynamics Observatory, pp. 17\u201340. Springer, New York (2011). https:\/\/doi.org\/10.1007\/978-1-4614-3673-7_3"},{"key":"27_CR13","unstructured":"Li, S., et al.: Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting. In: NeurIPS, vol. 32, pp. 5243\u20135253 (2019)"},{"issue":"4","key":"27_CR14","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1002\/2016SW001579","volume":"15","author":"S Murray","year":"2017","unstructured":"Murray, S., Bingham, S., Sharpe, M., Jackson, D.: Flare forecasting at the met office space weather operations centre. Space Weather 15(4), 577\u2013588 (2017)","journal-title":"Space Weather"},{"issue":"2","key":"27_CR15","doi-asserted-by":"publisher","first-page":"150","DOI":"10.3847\/1538-4357\/aba2f2","volume":"899","author":"N Nishizuka","year":"2020","unstructured":"Nishizuka, N., Kubo, Y., Sugiura, K., Den, M., Ishii, M.: Reliable probability forecast of solar flares: deep flare net-reliable (DeFN-R). Astrophys. J. 899(2), 150 (2020)","journal-title":"Astrophys. J."},{"issue":"2","key":"27_CR16","doi-asserted-by":"publisher","first-page":"156","DOI":"10.3847\/1538-4357\/835\/2\/156","volume":"835","author":"N Nishizuka","year":"2017","unstructured":"Nishizuka, N., Sugiura, K., Kubo, Y., Den, M., et al.: Solar flare prediction model with three machine-learning algorithms using ultraviolet brightening and vector magnetograms. Astrophys. J. 835(2), 156 (2017)","journal-title":"Astrophys. J."},{"issue":"2","key":"27_CR17","doi-asserted-by":"publisher","first-page":"113","DOI":"10.3847\/1538-4357\/aab9a7","volume":"858","author":"N Nishizuka","year":"2018","unstructured":"Nishizuka, N., Sugiura, K., Kubo, Y., Den, M., Ishii, M.: Deep flare net (DeFN) model for solar flare prediction. Astrophys. J. 858(2), 113 (2018)","journal-title":"Astrophys. J."},{"issue":"6","key":"27_CR18","doi-asserted-by":"publisher","first-page":"1553","DOI":"10.1007\/s00521-015-1955-3","volume":"27","author":"BT Ong","year":"2016","unstructured":"Ong, B.T., Sugiura, K., Zettsu, K.: Dynamically pre-trained deep recurrent neural networks using environmental monitoring data for predicting PM2.5. Neural Comput. Appl. 27(6), 1553\u20131566 (2016)","journal-title":"Neural Comput. Appl."},{"issue":"2","key":"27_CR19","doi-asserted-by":"publisher","first-page":"91","DOI":"10.3847\/1538-4357\/aaed40","volume":"869","author":"E Park","year":"2018","unstructured":"Park, E., Moon, Y.-J., Shin, S., Yi, K., Lim, D., et al.: Application of the deep convolutional neural network to the forecast of solar flare occurrence using full-disk solar magnetograms. Astrophys. J. 869(2), 91 (2018)","journal-title":"Astrophys. J."},{"key":"27_CR20","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-1-4614-3673-7_2","volume-title":"The Solar Dynamics Observatory","author":"W Pesnell","year":"2011","unstructured":"Pesnell, W., Thompson, B., Chamberlin, P.: The Solar Dynamics Observatory (SDO). In: Chamberlin, P., Pesnell, W.D., Thompson, B. (eds.) The Solar Dynamics Observatory, pp. 3\u201315. Springer, New York (2011). https:\/\/doi.org\/10.1007\/978-1-4614-3673-7_2"},{"key":"27_CR21","unstructured":"Rangapuram, S., Seeger, M., Gasthaus, J., Stella, L., et al.: Deep state space models for time series forecasting. In: NeurIPS, vol. 31, pp. 7785\u20137794 (2018)"},{"key":"27_CR22","unstructured":"Re, S.: Solar storm; how to calculate insured\/reinsured losses? Space Weather Workshop (2016)"},{"issue":"3","key":"27_CR23","doi-asserted-by":"publisher","first-page":"1181","DOI":"10.1016\/j.ijforecast.2019.07.001","volume":"36","author":"D Salinas","year":"2020","unstructured":"Salinas, D., Flunkert, V., Gasthaus, J., Januschowski, T.: DeepAR: probabilistic forecasting with autoregressive recurrent networks. Int. J. Forecast. 36(3), 1181\u20131191 (2020)","journal-title":"Int. J. Forecast."},{"issue":"1","key":"27_CR24","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1007\/s11207-011-9834-2","volume":"275","author":"P Scherrer","year":"2012","unstructured":"Scherrer, P., et al.: The helioseismic and magnetic imager (HMI) investigation for the solar dynamics observatory (SDO). Sol. Phys. 275(1), 207\u2013227 (2012)","journal-title":"Sol. Phys."},{"issue":"2","key":"27_CR25","doi-asserted-by":"publisher","first-page":"50","DOI":"10.3847\/1538-4365\/ac249e","volume":"257","author":"R Tang","year":"2021","unstructured":"Tang, R., et al.: Solar flare prediction based on the fusion of multiple deep-learning models. Astrophys. J. Suppl. Ser. 257(2), 50 (2021)","journal-title":"Astrophys. J. Suppl. Ser."},{"issue":"4","key":"27_CR26","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1016\/S0169-2070(00)00065-0","volume":"16","author":"L Tashman","year":"2000","unstructured":"Tashman, L.: Out-of-sample tests of forecasting accuracy: an analysis and review. Int. J. Forecast. 16(4), 437\u2013450 (2000)","journal-title":"Int. J. Forecast."},{"key":"27_CR27","unstructured":"Vaswani, A., et al.: Attention is all you need. In: NeurIPS, vol. 30, pp. 5998\u20136008 (2017)"},{"key":"27_CR28","unstructured":"Wen, R., Torkkola, K., Narayanaswamy, B., Madeka, D.: A Multi-horizon Quantile Recurrent Forecaster. arXiv preprint arXiv:1711.11053 (2017)"},{"key":"27_CR29","unstructured":"Wu, S., Xiao, X., Ding, Q., Zhao, P., et al.: Adversarial sparse transformer for time series forecasting. In: NeurIPS, vol. 33, pp. 17105\u201317115 (2020)"},{"key":"27_CR30","doi-asserted-by":"crossref","unstructured":"Zhou, H., Zhang, S., Peng, J., Zhang, S., et al.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: AAAI, pp. 11106\u201311115 (2021)","DOI":"10.1609\/aaai.v35i12.17325"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ACCV 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-26284-5_27","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,22]],"date-time":"2023-02-22T08:15:30Z","timestamp":1677053730000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-26284-5_27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031262838","9783031262845"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-26284-5_27","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"23 February 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asian Conference on Computer Vision","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":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 December 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 December 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"accv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.accv2022.org","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT Microsoft","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"836","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"277","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"33% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.6","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"For the ACCV 2022 workshops 25 papers have been accepted from 40 submissions","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}