{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T10:39:22Z","timestamp":1776335962563,"version":"3.51.2"},"reference-count":59,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2018,8,31]],"date-time":"2018-08-31T00:00:00Z","timestamp":1535673600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100004744","name":"Innoviris","doi-asserted-by":"publisher","award":["BruFence: Scalable machine learning for automating defense system"],"award-info":[{"award-number":["BruFence: Scalable machine learning for automating defense system"]}],"id":[{"id":"10.13039\/501100004744","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004744","name":"Innoviris","doi-asserted-by":"publisher","award":["BruFence: Scalable machine learning for automating defense system"],"award-info":[{"award-number":["BruFence: Scalable machine learning for automating defense system"]}],"id":[{"id":"10.13039\/501100004744","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Worldline","award":["ULB-Worldline Collaboration Agreement"],"award-info":[{"award-number":["ULB-Worldline Collaboration Agreement"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Data Sci Anal"],"published-print":{"date-parts":[[2019,6]]},"DOI":"10.1007\/s41060-018-0150-x","type":"journal-article","created":{"date-parts":[[2018,8,31]],"date-time":"2018-08-31T05:52:39Z","timestamp":1535694759000},"page":"311-329","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Batch and incremental dynamic factor machine learning for multivariate and multi-step-ahead forecasting"],"prefix":"10.1007","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0257-4537","authenticated-orcid":false,"given":"Jacopo","family":"De Stefani","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5679-7758","authenticated-orcid":false,"given":"Yann-A\u00ebl","family":"Le Borgne","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6970-9825","authenticated-orcid":false,"given":"Olivier","family":"Caelen","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9058-1042","authenticated-orcid":false,"given":"Dalila","family":"Hattab","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8621-316X","authenticated-orcid":false,"given":"Gianluca","family":"Bontempi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,8,31]]},"reference":[{"issue":"11","key":"150_CR1","doi-asserted-by":"publisher","first-page":"1593","DOI":"10.1089\/cmb.2008.0221","volume":"16","author":"M Andrecut","year":"2009","unstructured":"Andrecut, M.: Parallel GPU implementation of iterative PCA algorithms. J. Comput. Biol. 16(11), 1593\u20131599 (2009)","journal-title":"J. Comput. Biol."},{"key":"150_CR2","doi-asserted-by":"crossref","unstructured":"Arora, R., Cotter, A., Livescu, K., Srebro, N.: Stochastic optimization for PCA and PLS. In: 2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton), pp. 861\u2013868. IEEE (2012)","DOI":"10.1109\/Allerton.2012.6483308"},{"issue":"1","key":"150_CR3","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1109\/TNNLS.2015.2411629","volume":"27","author":"S Ben Taieb","year":"2016","unstructured":"Ben Taieb, S., Atiya, A.: A bias and variance analysis for multistep-ahead time series forecasting. IEEE Trans. Neural Netw. Learn. Syst. 27(1), 62\u201376 (2016)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"8","key":"150_CR4","doi-asserted-by":"publisher","first-page":"7067","DOI":"10.1016\/j.eswa.2012.01.039","volume":"39","author":"S Ben Taieb","year":"2012","unstructured":"Ben Taieb, S., Bontempi, G., Atiya, A., Sorjamaa, A.: A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition. Expert Syst. Appl. 39(8), 7067\u20137083 (2012)","journal-title":"Expert Syst. Appl."},{"key":"150_CR5","doi-asserted-by":"crossref","unstructured":"Ben Taieb, S., Bontempi, G., Sorjamaa, A., Lendasse, A.: Long-term prediction of time series by combining direct and mimo strategies. In: Proceedings of the 2009 IEEE International Joint Conference on Neural Networks, pp. 3054\u20133061. Atlanta, USA (2009)","DOI":"10.1109\/IJCNN.2009.5178802"},{"key":"150_CR6","doi-asserted-by":"publisher","first-page":"1950","DOI":"10.1016\/j.neucom.2009.11.030","volume":"73","author":"S Ben Taieb","year":"2010","unstructured":"Ben Taieb, S., Sorjamaa, A., Bontempi, G.: Multiple-output modelling for multi-step-ahead forecasting. Neurocomputing 73, 1950\u20131957 (2010)","journal-title":"Neurocomputing"},{"issue":"1","key":"150_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1561\/2200000006","volume":"2","author":"Y Bengio","year":"2009","unstructured":"Bengio, Y.: Learning deep architectures for AI. Found. Trends Mach. Learn. 2(1), 1\u2013127 (2009). https:\/\/doi.org\/10.1561\/2200000006","journal-title":"Found. Trends Mach. Learn."},{"key":"150_CR8","unstructured":"Blum, A., Rivest, R.L.: Training a 3-node neural network is np-complete. In: Proceedings of the 1st International Conference on Neural Information Processing Systems, pp. 494\u2013501. MIT Press (1988)"},{"key":"150_CR9","unstructured":"Bontempi, G.: Long term time series prediction with multi-input multi-output local learning. In: Proceedings of the 2nd European Symposium on Time Series Prediction (TSP), ESTSP08 pp. 145\u2013154 (2008)"},{"key":"150_CR10","doi-asserted-by":"publisher","unstructured":"Bontempi, G.: A Monte Carlo strategy for structured multiple-step-ahead time series prediction. In: 2014 International Joint Conference on Neural Networks (IJCNN), pp. 853\u2013858 (2014). https:\/\/doi.org\/10.1109\/IJCNN.2014.6889666","DOI":"10.1109\/IJCNN.2014.6889666"},{"key":"150_CR11","doi-asserted-by":"publisher","unstructured":"Bontempi, G., Ben\u00a0Taieb, S., Le\u00a0Borgne, Y.A.: Machine learning strategies for time series forecasting, pp. 62\u201377. Springer, Berlin (2013). https:\/\/doi.org\/10.1007\/978-3-642-36318-4_3","DOI":"10.1007\/978-3-642-36318-4_3"},{"issue":"7\/8","key":"150_CR12","doi-asserted-by":"publisher","first-page":"643","DOI":"10.1080\/002071799220830","volume":"72","author":"G Bontempi","year":"1999","unstructured":"Bontempi, G., Birattari, M., Bersini, H.: Lazy learning for modeling and control design. Int. J. Control 72(7\/8), 643\u2013658 (1999)","journal-title":"Int. J. Control"},{"key":"150_CR13","unstructured":"Bontempi, G., Birattari, M., Bersini, H.: Local learning for iterated time-series prediction. In: Bratko, I., Dzeroski, S. (eds.) Machine Learning: Proceedings of the Sixteenth International Conference, pp. 32\u201338. Morgan Kaufmann Publishers, San Francisco (1999)"},{"key":"150_CR14","doi-asserted-by":"crossref","unstructured":"Bontempi, G., Le\u00a0Borgne, Y.A., De\u00a0Stefani, J.: A dynamic factor machine learning method for multi-variate and multi-step-ahead forecasting. In: 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 222\u2013231. IEEE (2017)","DOI":"10.1109\/DSAA.2017.1"},{"issue":"3","key":"150_CR15","doi-asserted-by":"publisher","first-page":"689","DOI":"10.1016\/j.ijforecast.2010.09.004","volume":"27","author":"G Bontempi","year":"2011","unstructured":"Bontempi, G., Taieb, S.B.: Conditionally dependent strategies for multiple-step-ahead prediction in local learning. Int. J. Forecast. 27(3), 689\u2013699 (2011)","journal-title":"Int. J. Forecast."},{"issue":"4","key":"150_CR16","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1007\/BF00332918","volume":"59","author":"H Bourlard","year":"1988","unstructured":"Bourlard, H., Kamp, Y.: Auto-association by multilayer perceptrons and singular value decomposition. Biol. Cybern. 59(4), 291\u2013294 (1988)","journal-title":"Biol. Cybern."},{"issue":"2","key":"150_CR17","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1093\/biomet\/64.2.355","volume":"64","author":"G Box","year":"1977","unstructured":"Box, G., Tiao, G.: A canonical analysis of multiple time series. Biometrika 64(2), 355\u2013365 (1977)","journal-title":"Biometrika"},{"key":"150_CR18","doi-asserted-by":"crossref","unstructured":"Cheng, H., Tan, P.N., Gao, J., Scripps, J.: Multistep-ahead time series prediction. In: PAKDD, pp. 765\u2013774 (2006)","DOI":"10.1007\/11731139_89"},{"issue":"4","key":"150_CR19","doi-asserted-by":"publisher","first-page":"746","DOI":"10.1111\/j.1467-6419.2007.00518.x","volume":"21","author":"G Chevillon","year":"2007","unstructured":"Chevillon, G.: Direct multi-step estimation and forecasting. J. Econ. Surv. 21(4), 746\u2013785 (2007)","journal-title":"J. Econ. Surv."},{"key":"150_CR20","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1007\/978-3-319-44636-3_14","volume-title":"Automated Spark Clusters Deployment for Big Data with Standalone Applications Integration","author":"AM Fern\u00e1ndez","year":"2016","unstructured":"Fern\u00e1ndez, A.M., Torres, J.F., Troncoso, A., Mart\u00ednez-\u00c1lvarez, F.: Automated Spark Clusters Deployment for Big Data with Standalone Applications Integration, pp. 150\u2013159. Springer International Publishing, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-44636-3_14"},{"issue":"471","key":"150_CR21","doi-asserted-by":"publisher","first-page":"830","DOI":"10.1198\/016214504000002050","volume":"100","author":"M Forni","year":"2005","unstructured":"Forni, M., Hallin, M., Lippi, M., Reichlin, L.: The generalized dynamic factor model. J. Am. Stat. Assoc. 100(471), 830\u2013840 (2005). https:\/\/doi.org\/10.1198\/016214504000002050","journal-title":"J. Am. Stat. Assoc."},{"issue":"3","key":"150_CR22","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1111\/j.1467-6419.2009.00581.x","volume":"24","author":"P Franses","year":"2010","unstructured":"Franses, P., Legerstee, R.: A unifying view on multi-step forecasting using an autoregression. J. Econ. Surv. 24(3), 389\u2013401 (2010)","journal-title":"J. Econ. Surv."},{"key":"150_CR23","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1007\/978-3-319-59147-6_15","volume-title":"Scalable Forecasting Techniques Applied to Big Electricity Time Series","author":"A Galicia","year":"2017","unstructured":"Galicia, A., Torres, J.F., Mart\u00ednez-\u00c1lvarez, F., Troncoso, A.: Scalable Forecasting Techniques Applied to Big Electricity Time Series, pp. 165\u2013175. Springer International Publishing, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-59147-6_15"},{"key":"150_CR24","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1086\/296072","volume":"53","author":"MB Garman","year":"1980","unstructured":"Garman, M.B., Klass, M.J.: On the estimation of security price volatilities from historical data. J. Bus. 53, 67\u201378 (1980)","journal-title":"J. Bus."},{"key":"150_CR25","unstructured":"Gilbert, P.D.: State space and ARMA models : an overview of the equivalence. Bank of Canada, Ottawa (1993)"},{"key":"150_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v067.i02","volume":"67","author":"N Golyandina","year":"2015","unstructured":"Golyandina, N., Korobeynikov, A., Shlemov, A., Usevich, K.: Multivariate and 2d extensions of singular spectrum analysis with the RSSA package. J. Stat. Softw. 67, 1\u201378 (2015)","journal-title":"J. Stat. Softw."},{"key":"150_CR27","doi-asserted-by":"publisher","DOI":"10.1201\/9781420035841","volume-title":"Analysis of Time Series Structure: SSA and Related Techniques","author":"N Golyandina","year":"2001","unstructured":"Golyandina, N., Nekrutkin, V., Zhigljavsky, A.: Analysis of Time Series Structure: SSA and Related Techniques. CRC Press, Boca Raton (2001)"},{"key":"150_CR28","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-24797-2","volume-title":"Supervised Sequence Labelling with Recurrent Neural Networks","author":"A Graves","year":"2012","unstructured":"Graves, A.: Supervised Sequence Labelling with Recurrent Neural Networks. Springer, Berlin (2012)"},{"key":"150_CR29","first-page":"559","volume":"9","author":"M Guo","year":"1999","unstructured":"Guo, M., Bai, Z., An, H.: Multi-step prediction for nonlinear autoregressive models based on empirical distributions. Stat. Sin. 9, 559\u2013570 (1999)","journal-title":"Stat. Sin."},{"issue":"1","key":"150_CR30","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1007\/s11265-006-9773-6","volume":"45","author":"A Hegde","year":"2006","unstructured":"Hegde, A., Principe, J.C., Erdogmus, D., Ozertem, U., Rao, Y.N., Peddaneni, H.: Perturbation-based eigenvector updates for on-line principal components analysis and canonical correlation analysis. J. VLSI Signal Process. 45(1), 85\u201395 (2006)","journal-title":"J. VLSI Signal Process."},{"key":"150_CR31","volume-title":"Principal Component Analysis","author":"I Jolliffe","year":"2002","unstructured":"Jolliffe, I.: Principal Component Analysis. Springer, Berlin (2002)"},{"key":"150_CR32","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1016\/j.eswa.2018.01.037","volume":"100","author":"J Jurgovsky","year":"2018","unstructured":"Jurgovsky, J., Granitzer, M., Ziegler, K., Calabretto, S., Portier, P.E., He-Guelton, L., Caelen, O.: Sequence classification for credit-card fraud detection. Expert Syst. Appl. 100, 234\u2013245 (2018)","journal-title":"Expert Syst. Appl."},{"key":"150_CR33","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-73291-4","volume-title":"Introduction to Modern Time Series Analysis","author":"G Kirchgassner","year":"2007","unstructured":"Kirchgassner, G., Wolters, J.: Introduction to Modern Time Series Analysis. Springer, Berlin (2007)"},{"key":"150_CR34","doi-asserted-by":"crossref","unstructured":"Kline,D.M.:Methods for multi-step time series forecasting neural networks. In: Neural networks in business forecasting, pp. 226\u2013250. IGI Global, Hershey","DOI":"10.4018\/978-1-59140-176-6.ch012"},{"key":"150_CR35","unstructured":"Lipton, Z.C., Berkowitz, J., Elkan, C.: A critical review of recurrent neural networks for sequence learning (2015). arXiv preprint arXiv:1506.00019"},{"key":"150_CR36","doi-asserted-by":"crossref","unstructured":"Mat\u00edas, J.M.: Multi-output nonparametric regression. In: EPIA, pp. 288\u2013292 (2005)","DOI":"10.1007\/11595014_29"},{"key":"150_CR37","unstructured":"McNames, J.: A nearest trajectory strategy for time series prediction. In: Proceedings of the International Workshop on Advanced Black-Box Techniques for Nonlinear Modeling, pp. 112\u2013128. K.U. Leuven, Belgium (1998)"},{"issue":"1","key":"150_CR38","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1162\/0899766052530802","volume":"17","author":"CA Micchelli","year":"2005","unstructured":"Micchelli, C.A., Pontil, M.A.: On learning vector-valued functions. Neural Comput. 17(1), 177\u2013204 (2005). https:\/\/doi.org\/10.1162\/0899766052530802","journal-title":"Neural Comput."},{"key":"150_CR39","unstructured":"Mitliagkas, I., Caramanis, C., Jain, P.: Memory limited, streaming PCA. In: Advances in Neural Information Processing Systems, pp. 2886\u20132894 (2013)"},{"issue":"6","key":"150_CR40","doi-asserted-by":"publisher","first-page":"927","DOI":"10.1016\/S0893-6080(05)80089-9","volume":"5","author":"E Oja","year":"1992","unstructured":"Oja, E.: Principal components, minor components, and linear neural networks. Neural Netw. 5(6), 927\u2013935 (1992)","journal-title":"Neural Netw."},{"key":"150_CR41","unstructured":"Papadimitriou, S., Sun, J., Faloutsos, C.: Streaming pattern discovery in multiple time-series. In: Proceedings of the 31st International Conference on Very Large Data Bases, pp. 697\u2013708 (2005)"},{"key":"150_CR42","first-page":"433","volume-title":"Dimension Reduction in Multivariate Time Series","author":"D Pe\u00f1a","year":"2006","unstructured":"Pe\u00f1a, D., Poncela, P.: Dimension Reduction in Multivariate Time Series, pp. 433\u2013458. Birkh\u00e4user Boston, Boston (2006)"},{"key":"150_CR43","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1007\/978-3-319-40162-1_25","volume-title":"Finding Electric Energy Consumption Patterns in Big Time Series Data","author":"R Perez-Chacon","year":"2016","unstructured":"Perez-Chacon, R., Talavera-Llames, R.L., Martinez-Alvarez, F., Troncoso, A.: Finding Electric Energy Consumption Patterns in Big Time Series Data, pp. 231\u2013238. Springer International Publishing, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-40162-1_25"},{"issue":"2","key":"150_CR44","doi-asserted-by":"publisher","first-page":"478","DOI":"10.1257\/jel.41.2.478","volume":"41","author":"SH Poon","year":"2003","unstructured":"Poon, S.H., Granger, C.W.: Forecasting volatility in financial markets: a review. J. Econ. Lit. 41(2), 478\u2013539 (2003)","journal-title":"J. Econ. Lit."},{"issue":"6","key":"150_CR45","doi-asserted-by":"publisher","first-page":"1456","DOI":"10.1109\/72.728395","volume":"9","author":"E Saad","year":"1998","unstructured":"Saad, E., Prokhorov, D., Wunsch, D.: Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks. IEEE Trans. Neural Netw. 9(6), 1456\u20131470 (1998). https:\/\/doi.org\/10.1109\/72.728395","journal-title":"IEEE Trans. Neural Netw."},{"issue":"6","key":"150_CR46","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1016\/0893-6080(89)90044-0","volume":"2","author":"TD Sanger","year":"1989","unstructured":"Sanger, T.D.: Optimal unsupervised learning in a single-layer linear feedforward neural network. Neural Netw. 2(6), 459\u2013473 (1989)","journal-title":"Neural Netw."},{"issue":"16\u201318","key":"150_CR47","doi-asserted-by":"publisher","first-page":"2861","DOI":"10.1016\/j.neucom.2006.06.015","volume":"70","author":"A Sorjamaa","year":"2007","unstructured":"Sorjamaa, A., Hao, J., Reyhani, N., Ji, Y., Lendasse, A.: Methodology for long-term prediction of time series. Neurocompuing 70(16\u201318), 2861\u20132869 (2007). https:\/\/doi.org\/10.1016\/j.neucom.2006.06.015","journal-title":"Neurocompuing"},{"issue":"460","key":"150_CR48","doi-asserted-by":"publisher","first-page":"1167","DOI":"10.1198\/016214502388618960","volume":"97","author":"J Stock","year":"2002","unstructured":"Stock, J., Watson, M.: Forecasting using principal components from a large number of predictors. J. Am. Stat. Assoc. 97(460), 1167\u20131179 (2002)","journal-title":"J. Am. Stat. Assoc."},{"key":"150_CR49","volume-title":"Oxford Handbook of Economic Forecasting","author":"J Stock","year":"2010","unstructured":"Stock, J., Watson, M.: Dynamic factor models. In: Clements, M., Hendry, D. (eds.) Oxford Handbook of Economic Forecasting. Oxford University Press, Oxford (2010)"},{"key":"150_CR50","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1007\/978-3-319-32034-2_15","volume-title":"A Nearest Neighbours-Based Algorithm for Big Time Series Data Forecasting","author":"RL Talavera-Llames","year":"2016","unstructured":"Talavera-Llames, R.L., P\u00e9rez-Chac\u00f3n, R., Mart\u00ednez-Ballesteros, M., Troncoso, A., Mart\u00ednez-\u00c1lvarez, F.: A Nearest Neighbours-Based Algorithm for Big Time Series Data Forecasting, pp. 174\u2013185. Springer International Publishing, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-32034-2_15"},{"issue":"4","key":"150_CR51","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1016\/S0169-2070(00)00065-0","volume":"16","author":"LJ Tashman","year":"2000","unstructured":"Tashman, L.J.: Out-of-sample tests of forecasting accuracy: an analysis and review. Int. J. Forecast. 16(4), 437\u2013450 (2000). https:\/\/doi.org\/10.1016\/S0169-2070(00)00065-0 . (The M3- Competition)","journal-title":"Int. J. Forecast."},{"issue":"4","key":"150_CR52","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1016\/S0169-2070(00)00065-0","volume":"16","author":"LJ Tashman","year":"2000","unstructured":"Tashman, L.J.: 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":"150_CR53","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4684-7888-4","volume-title":"Threshold Models in Nonlinear Time Series Analysis","author":"H Tong","year":"1983","unstructured":"Tong, H.: Threshold Models in Nonlinear Time Series Analysis. Springer, Berlin (1983)"},{"key":"150_CR54","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1007\/978-3-319-59773-7_21","volume-title":"Deep Learning-Based Approach for Time Series Forecasting with Application to Electricity Load","author":"JF Torres","year":"2017","unstructured":"Torres, J.F., Fern\u00e1ndez, A.M., Troncoso, A., Mart\u00ednez-\u00c1lvarez, F.: Deep Learning-Based Approach for Time Series Forecasting with Application to Electricity Load, pp. 203\u2013212. Springer International Publishing, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-59773-7_21"},{"key":"150_CR55","volume-title":"Multivariate Time Series Analysis with R and Financial Applications","author":"RS Tsay","year":"2014","unstructured":"Tsay, R.S.: Multivariate Time Series Analysis with R and Financial Applications. Wiley, Hoboken (2014)"},{"key":"150_CR56","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jbi.2017.02.010","volume":"68","author":"S Tuarob","year":"2017","unstructured":"Tuarob, S., Tucker, C.S., Kumara, S., Giles, C.L., Pincus, A.L., Conroy, D.E., Ram, N.: How are you feeling?: a personalized methodology for predicting mental states from temporally observable physical and behavioral information. J. Biomed. Inform. 68, 1\u201319 (2017). https:\/\/doi.org\/10.1016\/j.jbi.2017.02.010","journal-title":"J. Biomed. Inform."},{"key":"150_CR57","first-page":"3371","volume":"11","author":"P Vincent","year":"2010","unstructured":"Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P.A.: Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11, 3371\u20133408 (2010)","journal-title":"J. Mach. Learn. Res."},{"key":"150_CR58","volume-title":"Time Series Prediction: forecasting the future and understanding the past","author":"A Weigend","year":"1994","unstructured":"Weigend, A., Gershenfeld, N.: Time Series Prediction: forecasting the future and understanding the past. Addison Wesley, Harlow (1994)"},{"issue":"8","key":"150_CR59","doi-asserted-by":"publisher","first-page":"1034","DOI":"10.1109\/TPAMI.2003.1217609","volume":"25","author":"J Weng","year":"2003","unstructured":"Weng, J., Zhang, Y., Hwang, W.S.: Candid covariance-free incremental principal component analysis. IEEE Trans. Pattern Anal. Mach. Intell. 25(8), 1034\u20131040 (2003)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["International Journal of Data Science and Analytics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s41060-018-0150-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s41060-018-0150-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s41060-018-0150-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,9]],"date-time":"2024-07-09T20:10:42Z","timestamp":1720555842000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s41060-018-0150-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,8,31]]},"references-count":59,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2019,6]]}},"alternative-id":["150"],"URL":"https:\/\/doi.org\/10.1007\/s41060-018-0150-x","relation":{},"ISSN":["2364-415X","2364-4168"],"issn-type":[{"value":"2364-415X","type":"print"},{"value":"2364-4168","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,8,31]]},"assertion":[{"value":"14 June 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 August 2018","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 August 2018","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"On behalf of all authors, the corresponding author states that there is no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}