{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T16:32:06Z","timestamp":1773246726411,"version":"3.50.1"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031234798","type":"print"},{"value":"9783031234804","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\/978-3-031-23480-4_13","type":"book-chapter","created":{"date-parts":[[2023,1,23]],"date-time":"2023-01-23T09:09:35Z","timestamp":1674464975000},"page":"152-164","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Improving Solar Flare Prediction by\u00a0Time Series Outlier Detection"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9176-5273","authenticated-orcid":false,"given":"Junzhi","family":"Wen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Md Reazul","family":"Islam","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1631-5336","authenticated-orcid":false,"given":"Azim","family":"Ahmadzadeh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9598-8207","authenticated-orcid":false,"given":"Rafal A.","family":"Angryk","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,1,24]]},"reference":[{"issue":"2","key":"13_CR1","doi-asserted-by":"publisher","first-page":"23","DOI":"10.3847\/1538-4365\/abec88","volume":"254","author":"A Ahmadzadeh","year":"2021","unstructured":"Ahmadzadeh, A., et al.: How to train your flare prediction model: revisiting robust sampling of rare events. Astrophys. J. Suppl. Ser. 254(2), 23 (2021). https:\/\/doi.org\/10.3847\/1538-4365\/abec88","journal-title":"Astrophys. J. Suppl. Ser."},{"key":"13_CR2","doi-asserted-by":"publisher","unstructured":"Angryk, R.A., et al.: Multivariate time series dataset for space weather data analytics. Nat. Sci. Data 7(1), 227 (2020). https:\/\/doi.org\/10.1038\/s41597-020-0548-x","DOI":"10.1038\/s41597-020-0548-x"},{"key":"13_CR3","doi-asserted-by":"crossref","unstructured":"Audibert, J., et al.: USAD: unsupervised anomaly detection on multivariate time series. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining pp. 3395\u20133404 (2020)","DOI":"10.1145\/3394486.3403392"},{"issue":"1","key":"13_CR4","doi-asserted-by":"publisher","first-page":"S01001","DOI":"10.1029\/2007SW000337","volume":"6","author":"CC Balch","year":"2008","unstructured":"Balch, C.C.: Updated verification of the Space Weather Prediction Center\u2019s solar energetic particle prediction model. Space Weather 6(1), S01001 (2008). https:\/\/doi.org\/10.1029\/2007SW000337","journal-title":"Space Weather"},{"issue":"2","key":"13_CR5","doi-asserted-by":"publisher","first-page":"463","DOI":"10.1007\/s10115-017-1067-8","volume":"54","author":"SE Benkabou","year":"2018","unstructured":"Benkabou, S.E., et al.: Unsupervised outlier detection for time series by entropy and dynamic time warping. Knowl. Inf. Syst. 54(2), 463\u2013486 (2018)","journal-title":"Knowl. Inf. Syst."},{"key":"13_CR6","doi-asserted-by":"publisher","unstructured":"Benvenuto, F., et al.: A Hybrid supervised\/unsupervised machine learning approach to solar flare prediction. Astrophys. J. 853(1), 90 (2018). https:\/\/doi.org\/10.3847\/1538-4357\/aaa23c","DOI":"10.3847\/1538-4357\/aaa23c"},{"key":"13_CR7","doi-asserted-by":"publisher","unstructured":"Bloomfield, D.S., et al.: Toward Reliable Benchmarking of Solar Flare Forecasting Methods. Astrophys. J. Lett. 747(2), L41 (2012). https:\/\/doi.org\/10.1088\/2041-8205\/747\/2\/L41","DOI":"10.1088\/2041-8205\/747\/2\/L41"},{"issue":"2","key":"13_CR8","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1088\/0004-637x\/798\/2\/135","volume":"798","author":"MG Bobra","year":"2015","unstructured":"Bobra, M.G., Couvidat, S.: Solar flare prediction usingsdo\/HMI vector magnetic field data with a machine-learning algorithm. Astrophys. J. 798(2), 135 (2015). https:\/\/doi.org\/10.1088\/0004-637x\/798\/2\/135","journal-title":"Astrophys. J."},{"key":"13_CR9","doi-asserted-by":"publisher","unstructured":"Budalakoti, S., et al.: Anomaly detection and diagnosis algorithms for discrete symbol sequences with applications to airline safety. IEEE Trans. Syst. Man Cybernet. Part C (Appl. Rev.) 39(1), 101\u2013113 (2009). https:\/\/doi.org\/10.1109\/TSMCC.2008.2007248","DOI":"10.1109\/TSMCC.2008.2007248"},{"key":"13_CR10","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"296","DOI":"10.1007\/978-3-030-87986-0_26","volume-title":"Artificial Intelligence and Soft Computing","author":"Y Chen","year":"2021","unstructured":"Chen, Y., Kempton, D.J., Ahmadzadeh, A., Angryk, R.A.: Towards synthetic multivariate time series generation for flare forecasting. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds.) ICAISC 2021. LNCS (LNAI), vol. 12854, pp. 296\u2013307. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87986-0_26"},{"issue":"7","key":"13_CR11","doi-asserted-by":"publisher","first-page":"6481","DOI":"10.1109\/JIOT.2019.2958185","volume":"7","author":"AA Cook","year":"2020","unstructured":"Cook, A.A., et al.: Anomaly detection for IoT time-series data: a survey. IEEE Internet Things J. 7(7), 6481\u20136494 (2020). https:\/\/doi.org\/10.1109\/JIOT.2019.2958185","journal-title":"IEEE Internet Things J."},{"key":"13_CR12","unstructured":"Cuturi, M.: Fast global alignment kernels. In: Proceedings of the 28th International Conference on Machine Learning (ICML-2011), pp. 929\u2013936 (2011)"},{"key":"13_CR13","unstructured":"Guo, Y., et al.: Multidimensional time series anomaly detection: a GRU-based gaussian mixture variational autoencoder approach. In: Proceedings of The 10th Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 95, pp. 97\u2013112. PMLR (14\u201316 November 2018). https:\/\/proceedings.mlr.press\/v95\/guo18a.html"},{"issue":"9","key":"13_CR14","doi-asserted-by":"publisher","first-page":"2250","DOI":"10.1109\/TKDE.2013.184","volume":"26","author":"M Gupta","year":"2013","unstructured":"Gupta, M., et al.: Outlier detection for temporal data: a survey. IEEE Trans. Knowl. Data Eng. 26(9), 2250\u20132267 (2013)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"13_CR15","doi-asserted-by":"publisher","unstructured":"Han, J., et al.: 3 - data preprocessing. In: Data Mining, 3rd edn., pp. 83\u2013124. The Morgan Kaufmann Series in Data Management Systems,3rd edn. Morgan Kaufmann, Boston, (2012). https:\/\/doi.org\/10.1016\/B978-0-12-381479-1.00003-4","DOI":"10.1016\/B978-0-12-381479-1.00003-4"},{"key":"13_CR16","unstructured":"Hanssen, A., Kuipers, W.: On the relationship between the frequency of rain and various meteorological parameters: (with reference to the problem ob objective forecasting). Koninkl. Nederlands Meterologisch Institut. Mededelingen en Verhandelingen, Staatsdrukkerij- en Uitgeverijbedrijf (1965). https:\/\/books.google.com\/books?id=nTZ8OgAACAAJ"},{"key":"13_CR17","doi-asserted-by":"publisher","unstructured":"Hostetter, M., et al.: Understanding the impact of statistical time series features for flare prediction analysis. In: 2019 IEEE International Conference on Big Data (Big Data), pp. 4960\u20134966 (2019). https:\/\/doi.org\/10.1109\/BigData47090.2019.9006116","DOI":"10.1109\/BigData47090.2019.9006116"},{"key":"13_CR18","doi-asserted-by":"publisher","unstructured":"Jolliffe, I.T., et al.: Forecast Verification: A Practitioner\u2019s Guide in Atmospheric Science. John Wiley & Sons (2012). https:\/\/doi.org\/10.1002\/9781119960003","DOI":"10.1002\/9781119960003"},{"key":"13_CR19","doi-asserted-by":"publisher","unstructured":"Liu, F.T., et al.: Isolation forest. In: 2008 Eighth IEEE International Conference on Data Mining. pp. 413\u2013422 (2008). https:\/\/doi.org\/10.1109\/ICDM.2008.17","DOI":"10.1109\/ICDM.2008.17"},{"key":"13_CR20","doi-asserted-by":"publisher","unstructured":"Martens, P.: Solar flares: preflare phase. In: Murdin, P. (ed.) Encyclopedia of Astronomy and Astrophysics, p. 2288 (2000). https:\/\/doi.org\/10.1888\/0333750888\/2288","DOI":"10.1888\/0333750888\/2288"},{"key":"13_CR21","doi-asserted-by":"publisher","unstructured":"Massone, A.M., et al.: Chapter 14 - machine learning for flare forecasting. In: Machine Learning Techniques for Space Weather, pp. 355\u2013364. Elsevier (2018). https:\/\/doi.org\/10.1016\/B978-0-12-811788-0.00014-7","DOI":"10.1016\/B978-0-12-811788-0.00014-7"},{"key":"13_CR22","doi-asserted-by":"publisher","unstructured":"Sadykov, V.M., Kosovichev, A.G.: Relationships between characteristics of the line-of-sight magnetic field and solar flare forecasts. Astrophys. J. 849(2), 148 (2017). https:\/\/doi.org\/10.3847\/1538-4357\/aa9119","DOI":"10.3847\/1538-4357\/aa9119"},{"issue":"1","key":"13_CR23","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1016\/S0034-4257(97)00083-7","volume":"62","author":"SV Stehman","year":"1997","unstructured":"Stehman, S.V.: Selecting and interpreting measures of thematic classification accuracy. Remote Sens. Environ. 62(1), 77\u201389 (1997). https:\/\/doi.org\/10.1016\/S0034-4257(97)00083-7","journal-title":"Remote Sens. Environ."},{"key":"13_CR24","unstructured":"Tavenard, R., et al.: Tslearn, a machine learning toolkit for time series data. J. Mach. Learn. Res. 21(118), 1\u20136 (2020). http:\/\/jmlr.org\/papers\/v21\/20-091.html"},{"key":"13_CR25","doi-asserted-by":"crossref","unstructured":"Zhang, C., et al.: A deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 1409\u20131416 (2019)","DOI":"10.1609\/aaai.v33i01.33011409"},{"key":"13_CR26","doi-asserted-by":"crossref","unstructured":"Zhou, C., Paffenroth, R.C.: Anomaly detection with robust deep autoencoders. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 665\u2013674 (2017)","DOI":"10.1145\/3097983.3098052"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence and Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-23480-4_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,23]],"date-time":"2023-01-23T09:11:50Z","timestamp":1674465110000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-23480-4_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031234798","9783031234804"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-23480-4_13","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":"24 January 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICAISC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Intelligence and Soft Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Zakopane","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Poland","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":"19 June 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 June 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icaisc2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icaisc.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}