{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T11:39:58Z","timestamp":1777289998658,"version":"3.51.4"},"reference-count":42,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2012,9,1]],"date-time":"2012-09-01T00:00:00Z","timestamp":1346457600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2012,9]]},"abstract":"<jats:p>Many applications naturally involve time series data and the vector autoregression (VAR), and the structural VAR (SVAR) are dominant tools to investigate relations between variables in time series. In the first part of this work, we show that the SVAR method is incapable of identifying contemporaneous causal relations for Gaussian process. In addition, least squares estimators become unreliable when the scales of the problems are large and observations are limited. In the remaining part, we propose an approach to apply Bayesian network learning algorithms to identify SVARs from time series data in order to capture both temporal and contemporaneous causal relations, and avoid high-order statistical tests. The difficulty of applying Bayesian network learning algorithms to time series is that the sizes of the networks corresponding to time series tend to be large, and high-order statistical tests are required by Bayesian network learning algorithms in this case. To overcome the difficulty, we show that the search space of conditioning sets d-separating two vertices should be a subset of the Markov blankets. Based on this fact, we propose an algorithm enabling us to learn Bayesian networks locally, and make the largest order of statistical tests independent of the scales of the problems. Empirical results show that our algorithm outperforms existing methods in terms of both efficiency and accuracy.<\/jats:p>","DOI":"10.1145\/2337542.2337561","type":"journal-article","created":{"date-parts":[[2012,10,12]],"date-time":"2012-10-12T20:56:02Z","timestamp":1350075362000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Learning Causal Relations in Multivariate Time Series Data"],"prefix":"10.1145","volume":"3","author":[{"given":"Zhenxing","family":"Wang","sequence":"first","affiliation":[{"name":"The Chinese University of Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Laiwan","family":"Chan","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2012,9]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-842X.2004.00360.x"},{"key":"e_1_2_1_2_1","volume-title":"Proceedings of the 2nd European Conference on Artificial Intelligence in Medicine.","volume":"38","author":"Beinlich I. A.","unstructured":"Beinlich , I. A. , Suermondt , H. J. , Chavez , R. M. , and Cooper , G. F . 1989. The ALARM monitoring system: A case study with two probabilistic inference techniques for belief networks . In Proceedings of the 2nd European Conference on Artificial Intelligence in Medicine. Vol. 38 . London, Great Britain, 247--256. Beinlich, I. A., Suermondt, H. J., Chavez, R. M., and Cooper, G. F. 1989. The ALARM monitoring system: A case study with two probabilistic inference techniques for belief networks. In Proceedings of the 2nd European Conference on Artificial Intelligence in Medicine. Vol. 38. London, Great Britain, 247--256."},{"key":"e_1_2_1_3_1","volume-title":"Alternative explanations of the money-income correlation. NBER working papers","author":"Bernanke B. S.","year":"1842","unstructured":"Bernanke , B. S. 1986. Alternative explanations of the money-income correlation. NBER working papers 1842 , National Bureau of Economic Research, Inc. Bernanke, B. S. 1986. Alternative explanations of the money-income correlation. NBER working papers 1842, National Bureau of Economic Research, Inc."},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1007421730016"},{"key":"e_1_2_1_5_1","unstructured":"Blanchard O. J. and Watson M. W. 1987. Are business cycles all alike? NBER working paper 1392 National Bureau of Economic Research. Blanchard O. J. and Watson M. W. 1987. Are business cycles all alike? NBER working paper 1392 National Bureau of Economic Research."},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.5018\/economics-ejournal.ja.2007-11"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0004-3702(02)00191-1"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/s001840000055"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1046\/j.0305-9049.2003.00087.x"},{"key":"e_1_2_1_10_1","unstructured":"Draper N. and Smith H. 1966. Applied Regression Analysis. Wiley New York. Draper N. and Smith H. 1966. Applied Regression Analysis . Wiley New York."},{"key":"e_1_2_1_11_1","first-page":"507","article-title":"Frequency distribution of the values of the correlation coefficient in samples from an indefinitely large population","volume":"10","author":"Fisher R. A.","year":"1915","unstructured":"Fisher , R. A. 1915 . Frequency distribution of the values of the correlation coefficient in samples from an indefinitely large population . Biometrika 10 , 4, 507 -- 521 . Fisher, R. A. 1915. Frequency distribution of the values of the correlation coefficient in samples from an indefinitely large population. Biometrika 10, 4, 507--521.","journal-title":"Biometrika"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/0165-1889(80)90069-X"},{"key":"e_1_2_1_13_1","first-page":"97","article-title":"Sparse causal discovery in multivariate time series","volume":"6","author":"Haufe S.","year":"2010","unstructured":"Haufe , S. , M\u00fcller , K.-R. , Nolte , G. , and Kr\u00e4mer , N. 2010 . Sparse causal discovery in multivariate time series . J. Mach. Learn. Res. - Proceedings Track 6 , 97 -- 106 . Haufe, S., M\u00fcller, K.-R., Nolte, G., and Kr\u00e4mer, N. 2010. Sparse causal discovery in multivariate time series. J. Mach. Learn. Res. - Proceedings Track 6, 97--106.","journal-title":"J. Mach. Learn. Res. - Proceedings Track"},{"key":"e_1_2_1_14_1","volume-title":"Proc. Syst. 22","author":"Henao R.","unstructured":"Henao , R. , and Winther , O . 2009. Bayesian sparse factor models and DAGs inference and comparison. Adv. Neural Inf . Proc. Syst. 22 . 736--744. Henao, R., and Winther, O. 2009. Bayesian sparse factor models and DAGs inference and comparison. Adv. Neural Inf. Proc. Syst. 22. 736--744."},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.2307\/2529336"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/1390156.1390210"},{"key":"e_1_2_1_17_1","volume-title":"Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI). 349--356","author":"Jensen A. L.","unstructured":"Jensen , A. L. and Jensen , F. V . 1996. Midas: An influence diagram for management of mildew in winter wheat . In Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI). 349--356 . Jensen, A. L. and Jensen, F. V. 1996. Midas: An influence diagram for management of mildew in winter wheat. In Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI). 349--356."},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1002\/net.3230200503"},{"key":"e_1_2_1_19_1","volume-title":"Proceedings of the International Conference on Machine Learning (ICML). 687--694","author":"Liu Y.","unstructured":"Liu , Y. , Niculescu-Mizil , A. , Lozano , A. C. , and Lu , Y . 2010. Learning temporal casual graphs for relational time-series analysis . In Proceedings of the International Conference on Machine Learning (ICML). 687--694 . Liu, Y., Niculescu-Mizil, A., Lozano, A. C., and Lu, Y. 2010. Learning temporal casual graphs for relational time-series analysis. In Proceedings of the International Conference on Machine Learning (ICML). 687--694."},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/1557019.1557085"},{"key":"e_1_2_1_21_1","volume-title":"Proc. Syst. 12","author":"Margaritis D.","unstructured":"Margaritis , D. and Thrun , S . 1999. Bayesian network induction via local neighborhoods. Adv. Neural Inf . Proc. Syst. 12 . MIT Press, 505--511. Margaritis, D. and Thrun, S. 1999. Bayesian network induction via local neighborhoods. Adv. Neural Inf. Proc. Syst. 12. MIT Press, 505--511."},{"key":"e_1_2_1_22_1","volume-title":"Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI). 411--418","author":"Meek C.","year":"1995","unstructured":"Meek , C. 1995 . Strong completeness and faithfulness in Bayesian networks . In Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI). 411--418 . Meek, C. 1995. Strong completeness and faithfulness in Bayesian networks. In Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI). 411--418."},{"key":"e_1_2_1_23_1","doi-asserted-by":"crossref","unstructured":"Moneta A. and Spirtes P. 2006. Graphical models for the identification of causal structures in multivariate time series models. In JCIS. Moneta A. and Spirtes P. 2006. Graphical models for the identification of causal structures in multivariate time series models. In JCIS .","DOI":"10.2991\/jcis.2006.171"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553470"},{"key":"e_1_2_1_25_1","volume-title":"Proceedings of the 2nd International Conference on Principles of Knowledge Representation and Reasoning.","author":"Pearl J.","unstructured":"Pearl , J. and Verma , T . 1991. A theory of inferred causation . In Proceedings of the 2nd International Conference on Principles of Knowledge Representation and Reasoning. Pearl, J. and Verma, T. 1991. A theory of inferred causation. In Proceedings of the 2nd International Conference on Principles of Knowledge Representation and Reasoning."},{"key":"e_1_2_1_26_1","volume-title":"Proceedings of the International Symposium on Intelligent Data Analysis (IDA). 229--239","author":"Pellet J.-P.","unstructured":"Pellet , J.-P. and Elisseeff , A . 2007. A partial correlation-based algorithm for causal structure discovery with continuous variables . In Proceedings of the International Symposium on Intelligent Data Analysis (IDA). 229--239 . Pellet, J.-P. and Elisseeff, A. 2007. A partial correlation-based algorithm for causal structure discovery with continuous variables. In Proceedings of the International Symposium on Intelligent Data Analysis (IDA). 229--239."},{"key":"e_1_2_1_27_1","unstructured":"Pellet J.-P. and Elisseeff A. 2008. Using markov blankets for causal structure learning. J. Mach. Learn. Res. 9. Pellet J.-P. and Elisseeff A. 2008. Using markov blankets for causal structure learning. J. Mach. Learn. Res. 9 ."},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553477"},{"key":"e_1_2_1_29_1","volume-title":"Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI).","author":"Ramsey J.","unstructured":"Ramsey , J. , Zhang , J. , and Spirtes , P . 2006. Adjacency-faithfulness and conservative causal inference . In Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI). Ramsey, J., Zhang, J., and Spirtes, P. 2006. Adjacency-faithfulness and conservative causal inference. In Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI)."},{"key":"e_1_2_1_30_1","unstructured":"Richard J. and Wichern D. 2002. Applied Multivariate Statistical Analysis. Prentice-Hall Inc New Jersey. Richard J. and Wichern D. 2002. Applied Multivariate Statistical Analysis. Prentice-Hall Inc New Jersey."},{"key":"e_1_2_1_31_1","unstructured":"Shimizu S. Hoyer P. O. Hyv\u00e4rinen A. and Kerminen A. 2006. A linear non-gaussian acyclic model for causal discovery. J. Mach. Learn. Res. 7. Shimizu S. Hoyer P. O. Hyv\u00e4rinen A. and Kerminen A. 2006. A linear non-gaussian acyclic model for causal discovery. J. Mach. Learn. Res. 7 ."},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.2307\/1912017"},{"key":"e_1_2_1_33_1","doi-asserted-by":"crossref","unstructured":"Sims C. A. 1986. Are forecasting models usable for policy analysis? Quarterly Review Win 2--16. Sims C. A. 1986. Are forecasting models usable for policy analysis? Quarterly Review Win 2--16.","DOI":"10.21034\/qr.1011"},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1177\/089443939100900106"},{"key":"e_1_2_1_35_1","volume-title":"Proceedings of Advanced Computing for the Social Sciences.","author":"Spirtes P.","unstructured":"Spirtes , P. , Glymour , C. , and Scheines , R . 1990. From probability to causality . In Proceedings of Advanced Computing for the Social Sciences. Spirtes, P., Glymour, C., and Scheines, R. 1990. From probability to causality. In Proceedings of Advanced Computing for the Social Sciences."},{"key":"e_1_2_1_36_1","doi-asserted-by":"crossref","unstructured":"Spirtes P. Glymour C. and Scheines R. 1993. Causation Prediction and Search. Springer Verlag Berlin. Spirtes P. Glymour C. and Scheines R. 1993. Causation Prediction and Search . Springer Verlag Berlin.","DOI":"10.1007\/978-1-4612-2748-9"},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1177\/0049124198027002003"},{"key":"e_1_2_1_38_1","unstructured":"Swanson N. and Granger C. 1994. Impulse response functions based on causal approach to residual orthogonalization in vector autoregressions. Tech. rep. Swanson N. and Granger C. 1994. Impulse response functions based on causal approach to residual orthogonalization in vector autoregressions. Tech. rep."},{"key":"e_1_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/956750.956838"},{"key":"e_1_2_1_40_1","volume-title":"the 16th International FLAIRS Conference, St. 376--380","author":"Tsamardinos I.","unstructured":"Tsamardinos , I. , Aliferis , C. F. , and Statnikov , E . 2003b. Algorithms for large scale markov blanket discovery . In the 16th International FLAIRS Conference, St. 376--380 . Tsamardinos, I., Aliferis, C. F., and Statnikov, E. 2003b. Algorithms for large scale markov blanket discovery. In the 16th International FLAIRS Conference, St. 376--380."},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-006-6889-7"},{"key":"e_1_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/1835804.1835944"}],"container-title":["ACM Transactions on Intelligent Systems and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/2337542.2337561","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/2337542.2337561","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T08:49:00Z","timestamp":1750236540000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/2337542.2337561"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2012,9]]},"references-count":42,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2012,9]]}},"alternative-id":["10.1145\/2337542.2337561"],"URL":"https:\/\/doi.org\/10.1145\/2337542.2337561","relation":{},"ISSN":["2157-6904","2157-6912"],"issn-type":[{"value":"2157-6904","type":"print"},{"value":"2157-6912","type":"electronic"}],"subject":[],"published":{"date-parts":[[2012,9]]},"assertion":[{"value":"2011-05-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2011-12-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2012-09-01","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}