{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T12:02:56Z","timestamp":1743076976909,"version":"3.40.3"},"publisher-location":"Cham","reference-count":48,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030780852"},{"type":"electronic","value":"9783030780869"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-78086-9_16","type":"book-chapter","created":{"date-parts":[[2021,7,1]],"date-time":"2021-07-01T00:45:53Z","timestamp":1625100353000},"page":"205-220","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Warped Input Gaussian Processes for Time Series Forecasting"],"prefix":"10.1007","author":[{"given":"Igor","family":"Vinokur","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David","family":"Tolpin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,7,1]]},"reference":[{"issue":"1","key":"16_CR1","doi-asserted-by":"publisher","first-page":"28","DOI":"10.18201\/ijisae.83441","volume":"3","author":"AB Abdullah","year":"2015","unstructured":"Abdullah, A.B., Pillai, T.R., Cai, L.Z.: Intrusion detection forecasting using time series for improving cyber defence. Int. J. Intell. Syst. Appl. Eng. 3(1), 28\u201344 (2015). https:\/\/doi.org\/10.18201\/ijisae.83441","journal-title":"Int. J. Intell. Syst. Appl. Eng."},{"issue":"2","key":"16_CR2","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1016\/j.aej.2011.01.015","volume":"50","author":"E Almeshaiei","year":"2011","unstructured":"Almeshaiei, E., Soltan, H.: A methodology for electric power load forecasting. Alex. Eng. J. 50(2), 137\u2013144 (2011). https:\/\/doi.org\/10.1016\/j.aej.2011.01.015","journal-title":"Alex. Eng. J."},{"key":"16_CR3","unstructured":"Gonum: Gonum numerical packages (2017). http:\/\/gonum.org\/"},{"key":"16_CR4","doi-asserted-by":"crossref","unstructured":"Banerjee, A., Dunson, D., Tokdar, S.: Efficient Gaussian process regression for large data sets (2011)","DOI":"10.1093\/biomet\/ass068"},{"key":"16_CR5","volume-title":"Time Series Analysis, Forecasting and Control","author":"GEP Box","year":"1990","unstructured":"Box, G.E.P., Jenkins, G.: Time Series Analysis, Forecasting and Control. Holden-Day Inc., Alexandria (1990)"},{"key":"16_CR6","unstructured":"Bui, T.D., Hern\u00e1ndez-Lobato, J.M., Hern\u00e1ndez-Lobato, D., Li, Y., Turner, R.E.: Deep Gaussian processes for regression using approximate expectation propagation. In: Proceedings of the 33rd International Conference on International Conference on Machine Learning, ICML 2016, vol. 48, pp, 1472\u20131481. JMLR.org (2016)"},{"key":"16_CR7","doi-asserted-by":"crossref","unstructured":"Calandra, R., Peters, J., Rasmussen, C.E., Deisenroth, M.P.: Manifold Gaussian processes for regression. In: International Joint Conference on Neural Networks (IJCNN), pp. 3338\u20133345. IEEE (2016)","DOI":"10.1109\/IJCNN.2016.7727626"},{"key":"16_CR8","doi-asserted-by":"publisher","unstructured":"Chandola, V., Vatsavai, R.R.: A Gaussian process based online change detection algorithm for monitoring periodic time series. In: Proceedings of the 2011 SIAM International Conference on Data Mining, pp. 95\u2013106 (2011). https:\/\/doi.org\/10.1137\/1.9781611972818.9","DOI":"10.1137\/1.9781611972818.9"},{"key":"16_CR9","doi-asserted-by":"crossref","unstructured":"Clifton, L., Clifton, D.A., Pimentel, M.A.F., Watkinson, P.J., Tarassenko, L.: Gaussian process regression in vital-sign early warning systems. In: 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 6161\u20136164 (2012)","DOI":"10.1109\/EMBC.2012.6347400"},{"key":"16_CR10","unstructured":"Col\u00f2, G.: Anomaly detection for cyber security: time series forecasting and deep learning. Int. J. Sci. Res. Math. Stat. Sci. 7, 40\u201352 (2020). https:\/\/www.isroset.org\/journal\/IJSRMSS\/full_paper_view.php?paper_id=1741"},{"issue":"3","key":"16_CR11","doi-asserted-by":"publisher","first-page":"949","DOI":"10.1109\/JBHI.2019.2890823","volume":"23","author":"G Colopy","year":"2019","unstructured":"Colopy, G., Roberts, S., Clifton, D.: Gaussian processes for personalized interpretable volatility metrics in the step-down ward. IEEE J. Biomed. Health Inform. 23(3), 949\u2013959 (2019)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"16_CR12","doi-asserted-by":"publisher","unstructured":"Condon, E., He, A., Cukier, M.: Analysis of computer security incident data using time series models. In: Proceedings of the 2008 19th International Symposium on Software Reliability Engineering, ISSRE 2008, pp. 77\u201386. IEEE Computer Society (2008). https:\/\/doi.org\/10.1109\/ISSRE.2008.39","DOI":"10.1109\/ISSRE.2008.39"},{"key":"16_CR13","unstructured":"Damianou, A.: Deep Gaussian processes and variational propagation of uncertainty (2018)"},{"key":"16_CR14","unstructured":"Damianou, A., Lawrence, N.: Deep Gaussian processes. In: Carvalho, C.M., Ravikumar, P. (eds.) Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics. Proceedings of Machine Learning Research, 29 April\u201301 May 2013, vol. 31, pp. 207\u2013215. PMLR, Scottsdale (2013)"},{"issue":"1","key":"16_CR15","first-page":"1425","volume":"17","author":"AC Damianou","year":"2016","unstructured":"Damianou, A.C., Titsias, M.K., Lawrence, N.D.: Variational inference for latent variables and uncertain inputs in Gaussian processes. J. Mach. Learn. Res. 17(1), 1425\u20131486 (2016)","journal-title":"J. Mach. Learn. Res."},{"key":"16_CR16","doi-asserted-by":"crossref","unstructured":"Garnett, R., Osborne, M.A., Roberts, S.J.: Sequential Bayesian prediction in the presence of changepoints. In: Proceedings of the 26th Annual International Conference on Machine Learning, ICML 2009, pp. 345\u2013352. ACM, New York (2009)","DOI":"10.1145\/1553374.1553418"},{"key":"16_CR17","unstructured":"Gibbs, M.: Bayesian Gaussian processes for classification and regression. Ph.D. thesis, University of Cambridge, Cambridge (1997)"},{"key":"16_CR18","unstructured":"Goldberg, P.W., Williams, C.K.I., Bishop, C.M.: Regression with input-dependent noise: a Gaussian process treatment. In: Jordan, M.I., Kearns, M.J., Solla, S.A. (eds.) Advances in Neural Information Processing Systems 10, pp. 493\u2013499. MIT Press (1998)"},{"key":"16_CR19","doi-asserted-by":"crossref","unstructured":"Gordon, A.D., Henzinger, T.A., Nori, A.V., Rajamani, S.K.: Probabilistic programming. In: International Conference on Software Engineering (ICSE, FOSE Track) (2014)","DOI":"10.1145\/2593882.2593900"},{"key":"16_CR20","doi-asserted-by":"publisher","DOI":"10.1137\/1.9780898717761","volume-title":"Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation","author":"A Griewank","year":"2008","unstructured":"Griewank, A., Walther, A.: Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation, vol. 2. Society for Industrial and Applied Mathematics, Philadelphia (2008)"},{"issue":"1","key":"16_CR21","doi-asserted-by":"publisher","first-page":"388","DOI":"10.1016\/j.ijforecast.2020.06.008","volume":"37","author":"H Hewamalage","year":"2021","unstructured":"Hewamalage, H., Bergmeir, C., Bandara, K.: Recurrent neural networks for time series forecasting: current status and future directions. Int. J. Forecast. 37(1), 388\u2013427 (2021). https:\/\/doi.org\/10.1016\/j.ijforecast.2020.06.008","journal-title":"Int. J. Forecast."},{"key":"16_CR22","doi-asserted-by":"crossref","unstructured":"Kersting, K., Plagemann, C., Pfaff, P., Burgard, W.: Most likely heteroscedastic Gaussian process regression. In: International Conference on Machine Learning (ICML), Corvallis, Oregon, USA (2007)","DOI":"10.1145\/1273496.1273546"},{"key":"16_CR23","doi-asserted-by":"publisher","unstructured":"Le, Q.V., Smola, A.J., Canu, S.: Heteroscedastic Gaussian process regression. In: Proceedings of the 22Nd International Conference on Machine Learning, ICML 2005, pp. 489\u2013496. ACM, New York (2005). https:\/\/doi.org\/10.1145\/1102351.1102413","DOI":"10.1145\/1102351.1102413"},{"issue":"1","key":"16_CR24","doi-asserted-by":"publisher","first-page":"503","DOI":"10.1007\/BF01589116","volume":"45","author":"DC Liu","year":"1989","unstructured":"Liu, D.C., Nocedal, J.: On the limited memory BFGS method for large scale optimization. Math. Program. 45(1), 503\u2013528 (1989)","journal-title":"Math. Program."},{"key":"16_CR25","unstructured":"Loper, J., Blei, D., Cunningham, J.P., Paninski, L.: General linear-time inference for gaussian processes on one dimension (2020)"},{"key":"16_CR26","first-page":"133","volume":"168","author":"DJ MacKay","year":"1998","unstructured":"MacKay, D.J.: Introduction to Gaussian processes. NATO ASI Ser. F Comput. Syst. Sci. 168, 133\u2013166 (1998)","journal-title":"NATO ASI Ser. F Comput. Syst. Sci."},{"issue":"3","key":"16_CR27","doi-asserted-by":"publisher","first-page":"991","DOI":"10.1137\/17M1129179","volume":"6","author":"S Marmin","year":"2018","unstructured":"Marmin, S., Ginsbourger, D., Baccou, J., Liandrat, J.: Warped Gaussian processes and derivative-based sequential designs for functions with heterogeneous variations. SIAM\/ASA J. Uncertain. Quantif. 6(3), 991\u20131018 (2018). https:\/\/doi.org\/10.1137\/17M1129179","journal-title":"SIAM\/ASA J. Uncertain. Quantif."},{"key":"16_CR28","unstructured":"Mchutchon, A., Rasmussen, C.E.: Gaussian process training with input noise. In: Shawe-Taylor, J., Zemel, R.S., Bartlett, P.L., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 24, pp. 1341\u20131349. Curran Associates, Inc. (2011)"},{"key":"16_CR29","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"1209","DOI":"10.1007\/978-3-030-29513-4_87","volume-title":"Intelligent Systems and Applications","author":"RS Nirwan","year":"2020","unstructured":"Nirwan, R.S., Bertschinger, N.: Applications of Gaussian process latent variable models in finance. In: Bi, Y., Bhatia, R., Kapoor, S. (eds.) IntelliSys 2019. AISC, vol. 1038, pp. 1209\u20131221. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-29513-4_87"},{"key":"16_CR30","unstructured":"Paciorek, C.J., Schervish, M.J.: Nonstationary covariance functions for Gaussian process regression. In: Proceedings of the 16th International Conference on Neural Information Processing Systems, NIPS 2003, pp. 273\u2013280. MIT Press, Cambridge (2003)"},{"key":"16_CR31","unstructured":"Paszke, A., et al.: Automatic differentiation in PyTorch. In: NIPS Autodiff Workshop (2017)"},{"key":"16_CR32","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1007\/978-3-540-87481-2_14","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"C Plagemann","year":"2008","unstructured":"Plagemann, C., Kersting, K., Burgard, W.: Nonstationary Gaussian process regression using point estimates of local smoothness. In: Daelemans, W., Goethals, B., Morik, K. (eds.) ECML PKDD 2008. LNCS (LNAI), vol. 5212, pp. 204\u2013219. Springer, Heidelberg (2008). https:\/\/doi.org\/10.1007\/978-3-540-87481-2_14"},{"key":"16_CR33","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/3206.001.0001","volume-title":"Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)","author":"CE Rasmussen","year":"2005","unstructured":"Rasmussen, C.E., Williams, C.K.I.: Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning). The MIT Press, Cambridge (2005)"},{"issue":"1984","key":"16_CR34","doi-asserted-by":"publisher","first-page":"20110550","DOI":"10.1098\/rsta.2011.0550","volume":"371","author":"S Roberts","year":"2013","unstructured":"Roberts, S., Osborne, M., Ebden, M., Reece, S., Gibson, N., Aigrain, S.: Gaussian processes for time-series modelling. Philos. Trans. R. Soc. A: Math. Phys. Eng. Sci. 371(1984), 20110550 (2013)","journal-title":"Philos. Trans. R. Soc. A: Math. Phys. Eng. Sci."},{"key":"16_CR35","unstructured":"Saatchi, Y., Turner, R., Rasmussen, C.: Gaussian process change point models. In: Proceedings of the 27th Annual International Conference on Machine Learning, ICML 2010, pp. 927\u2013934 (2010)"},{"key":"16_CR36","unstructured":"Salimbeni, H., Deisenroth, M.: Doubly stochastic variational inference for deep Gaussian processes. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems 30, pp. 4588\u20134599. Curran Associates, Inc. (2017)"},{"issue":"417","key":"16_CR37","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1080\/01621459.1992.10475181","volume":"87","author":"PD Sampson","year":"1992","unstructured":"Sampson, P.D., Guttorp, P.: Nonparametric estimation of nonstationary spatial covariance structure. J. Am. Stat. Assoc. 87(417), 108\u2013119 (1992)","journal-title":"J. Am. Stat. Assoc."},{"key":"16_CR38","doi-asserted-by":"crossref","unstructured":"Seabold, S., Perktold, J.: Statsmodels: econometric and statistical modeling with python. In: 9th Python in Science Conference (2010)","DOI":"10.25080\/Majora-92bf1922-011"},{"key":"16_CR39","series-title":"Chemical Analysis Series","volume-title":"Air Monitoring by Spectroscopic Techniques","author":"M Sigrist","year":"1994","unstructured":"Sigrist, M.: Air Monitoring by Spectroscopic Techniques. Chemical Analysis Series, vol. 197. Wiley, New York (1994)"},{"key":"16_CR40","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.2517-6161.1985.tb01327.x","volume":"47","author":"B Silverman","year":"1985","unstructured":"Silverman, B.: Some aspects of the spline smoothing approach to non-parametric curve fitting. J. R. Stat. Soc. B 47, 1\u201352 (1985)","journal-title":"J. R. Stat. Soc. B"},{"key":"16_CR41","unstructured":"Snelson, E., Ghahramani, Z.: Variable noise and dimensionality reduction for sparse Gaussian processes. In: Proceedings of the Twenty-Second Conference on Uncertainty in Artificial Intelligence, UAI 2006, pp. 461\u2013468. AUAI Press, Arlington (2006)"},{"key":"16_CR42","unstructured":"Snoek, J., Swersky, K., Zemel, R., Adams, R.P.: Input warping for Bayesian optimization of non-stationary functions. In: Proceedings of the 31st International Conference on International Conference on Machine Learning, ICML 2014, vol. 32, pp. II-1674\u2013II-1682. JMLR.org (2014)"},{"key":"16_CR43","unstructured":"Tobar, F., Bui, T.D., Turner, R.E.: Learning stationary time series using Gaussian processes with nonparametric kernels. In: Cortes, C., Lawrence, N.D., Lee, D.D., Sugiyama, M., Garnett, R. (eds.) Advances in Neural Information Processing Systems 28, pp. 3501\u20133509. Curran Associates, Inc. (2015)"},{"key":"16_CR44","doi-asserted-by":"crossref","unstructured":"Tolpin, D.: Deployable probabilistic programming. In: Proceedings of the 2019 ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software, pp. 1\u201316. Onward! 2019. ACM, New York (2019)","DOI":"10.1145\/3359591.3359727"},{"key":"16_CR45","unstructured":"Tolpin, D.: GoGP, a library for probabilistic programming with Gaussian processes (2019). http:\/\/bitbucket.org\/dtolpin\/gogp"},{"key":"16_CR46","unstructured":"Wang, C., Neal, R.M.: Gaussian process regression with heteroscedastic or non-Gaussian residuals (2012)"},{"key":"16_CR47","unstructured":"Wang, K., Pleiss, G., Gardner, J., Tyree, S., Weinberger, K.Q., Wilson, A.G.: Exact Gaussian processes on a million data points. In: Wallach, H., Larochelle, H., Beygelzimer, A., d\u2019 Alch\u00e9-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 32. Curran Associates, Inc. (2019). https:\/\/proceedings.neurips.cc\/paper\/2019\/file\/01ce84968c6969bdd5d51c5eeaa3946a-Paper.pdf"},{"key":"16_CR48","unstructured":"Wu, Y., Hern\u00e1ndez-Lobato, J.M., Ghahramani, Z.: Gaussian process volatility model. In: Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N.D., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 27, pp. 1044\u20131052. Curran Associates, Inc. (2014)"}],"container-title":["Lecture Notes in Computer Science","Cyber Security Cryptography and Machine Learning"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-78086-9_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,3]],"date-time":"2024-09-03T01:53:56Z","timestamp":1725328436000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-78086-9_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030780852","9783030780869"],"references-count":48,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-78086-9_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"1 July 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CSCML","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Cyber Security Cryptography and Machine Learning","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Be'er Sheva","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Israel","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 July 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 July 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cscml2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.cs.bgu.ac.il\/~fradmin\/cscml21\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Open","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"48","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":"22","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":"13","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":"46% - 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","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1 keynote paper is also included.","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)"}}]}}