{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,11]],"date-time":"2025-07-11T10:53:41Z","timestamp":1752231221655,"version":"3.40.3"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319712451"},{"type":"electronic","value":"9783319712468"}],"license":[{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"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":[[2017]]},"DOI":"10.1007\/978-3-319-71246-8_33","type":"book-chapter","created":{"date-parts":[[2017,12,29]],"date-time":"2017-12-29T09:03:20Z","timestamp":1514538200000},"page":"544-558","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Forecasting and Granger Modelling with\u00a0Non-linear Dynamical Dependencies"],"prefix":"10.1007","author":[{"given":"Magda","family":"Gregorov\u00e1","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexandros","family":"Kalousis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"St\u00e9phane","family":"Marchand-Maillet","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2017,12,30]]},"reference":[{"key":"33_CR1","doi-asserted-by":"crossref","unstructured":"Arnold, A., Liu, Y., Abe, N.: Temporal causal modeling with graphical granger methods. In: Proceedings of 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD 2007 (2007)","DOI":"10.1145\/1281192.1281203"},{"key":"33_CR2","first-page":"1179","volume":"9","author":"F Bach","year":"2008","unstructured":"Bach, F.: Consistency of the group lasso and multiple kernel learning. J. Mach. Learn. Res. 9, 1179\u20131225 (2008)","journal-title":"J. Mach. Learn. Res."},{"key":"33_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1561\/2200000015","volume":"4","author":"F Bach","year":"2012","unstructured":"Bach, F., Jenatton, R., Mairal, J., Obozinski, G.: Optimization with sparsity-inducing penalties. Found. Trends Mach. Learn. 4, 1\u2013106 (2012)","journal-title":"Found. Trends Mach. Learn."},{"key":"33_CR4","doi-asserted-by":"crossref","unstructured":"Bahadori, M., Liu, Y.: An examination of practical granger causality inference. In: SIAM Conference on Data Mining (2013)","DOI":"10.1137\/1.9781611972832.52"},{"key":"33_CR5","doi-asserted-by":"crossref","unstructured":"Beck, A., Teboulle, M.: Gradient-based algorithms with applications to signal recovery. In: Convex Optimization in Signal Processing and Communications (2009)","DOI":"10.1017\/CBO9780511804458.003"},{"key":"33_CR6","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4899-0004-3","volume-title":"Time Series: Theory and Methods","author":"PJ Brockwell","year":"2006","unstructured":"Brockwell, P.J., Davis, R.A.: Time Series: Theory and Methods, 2nd edn. Springer Science+Business Media, LLC, New York (2006). https:\/\/doi.org\/10.1007\/978-1-4899-0004-3","edition":"2"},{"key":"33_CR7","first-page":"1615","volume":"9","author":"A Caponnetto","year":"2008","unstructured":"Caponnetto, A., Micchelli, C.A., Pontil, M., Ying, Y.: Universal multi-task kernels. Mach. Learn. Res. 9, 1615\u20131646 (2008)","journal-title":"Mach. Learn. Res."},{"key":"33_CR8","unstructured":"Dinuzzo, F., Ong, C.: Learning output kernels with block coordinate descent. In: International Conference on Machine Learning (ICML) (2011)"},{"key":"33_CR9","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1007\/s00440-011-0345-8","volume":"153","author":"M Eichler","year":"2012","unstructured":"Eichler, M.: Graphical modelling of multivariate time series. Probab. Theory Relat. Fields 153, 233\u2013268 (2012)","journal-title":"Probab. Theory Relat. Fields"},{"key":"33_CR10","doi-asserted-by":"publisher","first-page":"3097","DOI":"10.1162\/neco.2006.18.12.3097","volume":"18","author":"MO Franz","year":"2006","unstructured":"Franz, M.O., Sch\u00f6lkopf, B.: A unifying view of wiener and volterra theory and polynomial kernel regression. Neural Comput. 18, 3097\u20133118 (2006)","journal-title":"Neural Comput."},{"issue":"3","key":"33_CR11","doi-asserted-by":"publisher","first-page":"424","DOI":"10.2307\/1912791","volume":"37","author":"CWJ Granger","year":"1969","unstructured":"Granger, C.W.J.: Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37(3), 424\u2013438 (1969)","journal-title":"Econometrica"},{"key":"33_CR12","unstructured":"Jawanpuria, P., Lapin, M., Hein, M., Schiele, B.: Efficient output kernel learning for multiple tasks. In: NIPS (2015)"},{"key":"33_CR13","unstructured":"Kadri, H., Rakotomamonjy, A., Bach, F., Preux, P.: Multiple operator-valued kernel learning. In: NIPS (2012)"},{"key":"33_CR14","first-page":"27","volume":"5","author":"GGR Lanckriet","year":"2004","unstructured":"Lanckriet, G.G.R., Cristianini, N., Bartlett, P., Ghaoui, L.E., Jordan, M.I.: Learning the kernel matrix with semidefinite programming. J. Mach. Learn. Res. 5, 27\u201372 (2004)","journal-title":"J. Mach. Learn. Res."},{"key":"33_CR15","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1007\/s10994-014-5479-3","volume":"99","author":"N Lim","year":"2015","unstructured":"Lim, N., D\u2019Alch\u00e9-Buc, F., Auliac, C., Michailidis, G.: Operator-valued Kernel-based vector autoregressive models for network inference. Mach. Learn. 99, 489 (2015). https:\/\/doi.org\/10.1007\/s10994-014-5479-3","journal-title":"Mach. Learn."},{"key":"33_CR16","doi-asserted-by":"publisher","first-page":"i110","DOI":"10.1093\/bioinformatics\/btp199","volume":"25","author":"AC Lozano","year":"2009","unstructured":"Lozano, A.C., Abe, N., Liu, Y., Rosset, S.: Grouped graphical Granger modeling for gene expression regulatory networks discovery. Bioinformatics 25, i110\u2013i118 (2009). (Oxford, England)","journal-title":"Bioinformatics"},{"key":"33_CR17","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1162\/0899766052530802","volume":"17","author":"CA Micchelli","year":"2005","unstructured":"Micchelli, C.A., Pontil, M.: On learning vector-valued functions. Neural Comput. 17, 177\u2013204 (2005)","journal-title":"Neural Comput."},{"key":"33_CR18","doi-asserted-by":"publisher","first-page":"657","DOI":"10.1016\/j.automatica.2014.01.001","volume":"50","author":"G Pillonetto","year":"2014","unstructured":"Pillonetto, G., Dinuzzo, F., Chen, T., De Nicolao, G., Ljung, L.: Kernel methods in system identification, machine learning and function estimation: a survey. Automatica 50, 657\u2013682 (2014)","journal-title":"Automatica"},{"key":"33_CR19","unstructured":"Sindhwani, V., Minh, H.Q., Lozano, A.: Scalable matrix-valued kernel learning for high-dimensional nonlinear multivariate regression and granger causality. In: UAI (2013)"},{"key":"33_CR20","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-07028-5","volume-title":"Non-linear Time Series","author":"KF Turkman","year":"2014","unstructured":"Turkman, K.F., Scotto, M.G., de Zea Bermudez, P.: Non-linear Time Series. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-07028-5"},{"key":"33_CR21","unstructured":"Xu, Z., Jin, R., Yang, H., King, I., Lyu, M.R.: Simple and efficient multiple kernel learning by group lasso. In: International Conference on Machine Learning (ICML) (2010)"},{"key":"33_CR22","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1111\/j.1467-9868.2005.00532.x","volume":"68","author":"M Yuan","year":"2006","unstructured":"Yuan, M., Lin, Y.: Model selection and estimation in regression with grouped variables. J. Roy. Stat. Soc.: Ser. B (Stat. Methodol.) 68, 49\u201367 (2006)","journal-title":"J. Roy. Stat. Soc.: Ser. B (Stat. Methodol.)"},{"key":"33_CR23","unstructured":"Zhao, P., Rocha, G.: Grouped and hierarchical model selection through composite absolute penalties (2006)"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-71246-8_33","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,29]],"date-time":"2022-12-29T01:13:29Z","timestamp":1672276409000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-319-71246-8_33"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017]]},"ISBN":["9783319712451","9783319712468"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-71246-8_33","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2017]]},"assertion":[{"value":"30 December 2017","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Skopje","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Macedonia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2017","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2017","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2017","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2017","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/ecmlpkdd2017.ijs.si\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}