{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:16:09Z","timestamp":1750220169526,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":48,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,10,26]],"date-time":"2022-10-26T00:00:00Z","timestamp":1666742400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100004742","name":"Wells Fargo","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100004742","id-type":"DOI","asserted-by":"publisher"}]},{"name":"MIT-IBM Watson AI Lab"},{"name":"Refinitiv - LSEG"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,11,2]]},"DOI":"10.1145\/3533271.3561702","type":"proceedings-article","created":{"date-parts":[[2022,10,20]],"date-time":"2022-10-20T22:20:22Z","timestamp":1666304422000},"page":"480-488","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Knowledge Graph Guided Simultaneous Forecasting and Network Learning for Multivariate Financial Time Series"],"prefix":"10.1145","author":[{"given":"Shibal","family":"Ibrahim","sequence":"first","affiliation":[{"name":"Electrical Engineering and Computer Science, Massachusetts Institute of Technology, US"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenyu","family":"Chen","sequence":"additional","affiliation":[{"name":"Operations Research Center, Massachusetts Institute of Technology, US"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yada","family":"Zhu","sequence":"additional","affiliation":[{"name":"IBM Research, US"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pin-Yu","family":"Chen","sequence":"additional","affiliation":[{"name":"IBM Research, US"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Zhang","sequence":"additional","affiliation":[{"name":"IBM T. J. Watson Research, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rahul","family":"Mazumder","sequence":"additional","affiliation":[{"name":"Massachusetts Institute of Technology, US"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,10,26]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Mart\u00edn Abadi Ashish Agarwal Paul Barham 2015. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems.  Mart\u00edn Abadi Ashish Agarwal Paul Barham 2015. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems."},{"key":"e_1_3_2_1_2_1","unstructured":"S. Abu-El-Haija A. Kapoor B. Perozzi 2019. N-GCN: Multi-scale Graph Convolution for Semi-supervised Node Classification. In UAI.  S. Abu-El-Haija A. Kapoor B. Perozzi 2019. N-GCN: Multi-scale Graph Convolution for Semi-supervised Node Classification. In UAI."},{"key":"e_1_3_2_1_3_1","article-title":"Covariance Matrix Estimation under Total Positivity for Portfolio Selection*","volume":"20","author":"Agrawal Raj","year":"2020","unstructured":"Raj Agrawal , Uma Roy , and Caroline Uhler . 2020 . Covariance Matrix Estimation under Total Positivity for Portfolio Selection* . Journal of Financial Econometrics 20 , 2 (09 2020), 367\u2013389. Raj Agrawal, Uma Roy, and Caroline Uhler. 2020. Covariance Matrix Estimation under Total Positivity for Portfolio Selection*. Journal of Financial Econometrics 20, 2 (09 2020), 367\u2013389.","journal-title":"Journal of Financial Econometrics"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jeconom.2008.08.010"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/jae.2676"},{"key":"e_1_3_2_1_6_1","first-page":"1","article-title":"Generalized dynamic factor models and volatilities: recovering the market volatility shocks","volume":"19","author":"Barigozzi M.","year":"2015","unstructured":"M. Barigozzi and M. Hallin . 2015 . Generalized dynamic factor models and volatilities: recovering the market volatility shocks . The Econometrics Journal 19 , 1 (Nov. 2015), C33\u2013C60. M. Barigozzi and M. Hallin. 2015. Generalized dynamic factor models and volatilities: recovering the market volatility shocks. The Econometrics Journal 19, 1 (Nov. 2015), C33\u2013C60.","journal-title":"The Econometrics Journal"},{"key":"e_1_3_2_1_7_1","series-title":"SIAM journal on imaging sciences 2, 1","volume-title":"A fast iterative shrinkage-thresholding algorithm for linear inverse problems","author":"Beck Amir","year":"2009","unstructured":"Amir Beck and Marc Teboulle . 2009. A fast iterative shrinkage-thresholding algorithm for linear inverse problems . SIAM journal on imaging sciences 2, 1 ( 2009 ), 183\u2013202. Amir Beck and Marc Teboulle. 2009. A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM journal on imaging sciences 2, 1 (2009), 183\u2013202."},{"key":"e_1_3_2_1_8_1","first-page":"179","article-title":"Statistical analysis of non-lattice data","volume":"24","author":"Besag J.","year":"1975","unstructured":"J. Besag . 1975 . Statistical analysis of non-lattice data . Journal of the Royal Statistical Society: Series D (The Statistician) 24 , 3(1975), 179 \u2013 195 . J. Besag. 1975. Statistical analysis of non-lattice data. Journal of the Royal Statistical Society: Series D (The Statistician) 24, 3(1975), 179\u2013195.","journal-title":"Journal of the Royal Statistical Society: Series D (The Statistician)"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jeconom.2020.07.015"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3269269"},{"key":"e_1_3_2_1_11_1","unstructured":"D. Cheng F. Yang X. Wang Y. Zhang and L. Zhang. 2020. Knowledge Graph-Based Event Embedding Framework for Financial Quantitative Investments. In SIGIR \u201920. Association for Computing Machinery New York NY USA 2221\u20132230.  D. Cheng F. Yang X. Wang Y. Zhang and L. Zhang. 2020. Knowledge Graph-Based Event Embedding Framework for Financial Quantitative Investments. In SIGIR \u201920. Association for Computing Machinery New York NY USA 2221\u20132230."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1080\/10618600.2015.1092978"},{"volume-title":"Knowledge-Driven Stock Trend Prediction and Explanation via Temporal Convolutional Network","author":"Deng Shumin","key":"e_1_3_2_1_13_1","unstructured":"Shumin Deng , Ningyu Zhang , Wen Zhang , 2019. Knowledge-Driven Stock Trend Prediction and Explanation via Temporal Convolutional Network . Association for Computing Machinery , New York, NY, USA , 678\u2013685. Shumin Deng, Ningyu Zhang, Wen Zhang, 2019. Knowledge-Driven Stock Trend Prediction and Explanation via Temporal Convolutional Network. Association for Computing Machinery, New York, NY, USA, 678\u2013685."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"crossref","unstructured":"F. Diebold and K. Yilmaz. 2015. Financial and Macroeconomic Connectedness: A Network Approach to Measurement and Monitoring.  F. Diebold and K. Yilmaz. 2015. Financial and Macroeconomic Connectedness: A Network Approach to Measurement and Monitoring.","DOI":"10.1093\/acprof:oso\/9780199338290.001.0001"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jeconom.2014.04.012"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"crossref","unstructured":"R. Engle G. Gallo and M. Velucchi. 2012. Volatility Spillovers in East Asian Financial Markets: A Mem-Based Approach. Review of Economics and Statistics - REV ECON STATIST 0 (02 2012) 222\u2013223.  R. Engle G. Gallo and M. Velucchi. 2012. Volatility Spillovers in East Asian Financial Markets: A Mem-Based Approach. Review of Economics and Statistics - REV ECON STATIST 0 (02 2012) 222\u2013223.","DOI":"10.1162\/REST_a_00167"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jeconom.2008.09.017"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"crossref","unstructured":"J. Fan Y. Liao and H. Liu. 2016. An overview of the estimation of large covariance and precision matrices.  J. Fan Y. Liao and H. Liu. 2016. An overview of the estimation of large covariance and precision matrices.","DOI":"10.1111\/ectj.12061"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"crossref","unstructured":"J. Fan and Q. Yao. 2017. The elements of financial econometrics. Cambridge University Press.  J. Fan and Q. Yao. 2017. The elements of financial econometrics. Cambridge University Press.","DOI":"10.1017\/9781108120616"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3309547"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1093\/biostatistics\/kxm045"},{"key":"e_1_3_2_1_22_1","volume-title":"Modeling gene expression regulatory networks with the sparse vector autoregressive model. BMC systems biology 1, 1","author":"Fujita Andr\u00e9","year":"2007","unstructured":"Andr\u00e9 Fujita , Joao\u00a0 R Sato , Humberto\u00a0 M Garay-Malpartida , Rui Yamaguchi , Satoru Miyano , Mari\u00a0 C Sogayar , and Carlos\u00a0 E Ferreira . 2007. Modeling gene expression regulatory networks with the sparse vector autoregressive model. BMC systems biology 1, 1 ( 2007 ), 1\u201311. Andr\u00e9 Fujita, Joao\u00a0R Sato, Humberto\u00a0M Garay-Malpartida, Rui Yamaguchi, Satoru Miyano, Mari\u00a0C Sogayar, and Carlos\u00a0E Ferreira. 2007. Modeling gene expression regulatory networks with the sparse vector autoregressive model. BMC systems biology 1, 1 (2007), 1\u201311."},{"key":"e_1_3_2_1_23_1","volume-title":"Deep Learning for Time-Series Analysis. ArXiv","author":"Gamboa J.","year":"2017","unstructured":"J. Gamboa . 2017. Deep Learning for Time-Series Analysis. ArXiv ( 2017 ). J. Gamboa. 2017. Deep Learning for Time-Series Analysis. ArXiv (2017)."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.5555\/2789272.2912099"},{"volume-title":"Statistical learning with sparsity: the lasso and generalizations","author":"Hastie Trevor","key":"e_1_3_2_1_25_1","unstructured":"Trevor Hastie , Robert Tibshirani , and Martin Wainwright . 2015. Statistical learning with sparsity: the lasso and generalizations . CRC press . Trevor Hastie, Robert Tibshirani, and Martin Wainwright. 2015. Statistical learning with sparsity: the lasso and generalizations. CRC press."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_2_1_27_1","volume-title":"Estimating vector autoregressions with panel data. Econometrica: Journal of the econometric society","author":"Holtz-Eakin Douglas","year":"1988","unstructured":"Douglas Holtz-Eakin , Whitney Newey , and Harvey\u00a0 S Rosen . 1988. Estimating vector autoregressions with panel data. Econometrica: Journal of the econometric society ( 1988 ), 1371\u20131395. Douglas Holtz-Eakin, Whitney Newey, and Harvey\u00a0S Rosen. 1988. Estimating vector autoregressions with panel data. Econometrica: Journal of the econometric society (1988), 1371\u20131395."},{"key":"e_1_3_2_1_28_1","volume-title":"Estimation of a structural vector autoregression model using non-gaussianity.JMLR 11, 5","author":"Hyv\u00e4rinen Aapo","year":"2010","unstructured":"Aapo Hyv\u00e4rinen , Kun Zhang , Shohei Shimizu , 2010. Estimation of a structural vector autoregression model using non-gaussianity.JMLR 11, 5 ( 2010 ). Aapo Hyv\u00e4rinen, Kun Zhang, Shohei Shimizu, 2010. Estimation of a structural vector autoregression model using non-gaussianity.JMLR 11, 5 (2010)."},{"key":"e_1_3_2_1_29_1","unstructured":"Weiwei Jiang. 2020. Applications of deep learning in stock market prediction: recent progress. arXiv preprint arXiv:2003.01859(2020).  Weiwei Jiang. 2020. Applications of deep learning in stock market prediction: recent progress. arXiv preprint arXiv:2003.01859(2020)."},{"key":"e_1_3_2_1_30_1","volume-title":"Semi-Supervised Classification with Graph Convolutional Networks. In ICLR 2017","author":"Kipf N.","year":"2017","unstructured":"T.\u00a0 N. Kipf and M. Welling . 2017 . Semi-Supervised Classification with Graph Convolutional Networks. In ICLR 2017 , Toulon, France , April 24-26, 2017 . T.\u00a0N. Kipf and M. Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In ICLR 2017, Toulon, France, April 24-26, 2017."},{"volume-title":"Graphical models. Vol.\u00a017","author":"Lauritzen L","key":"e_1_3_2_1_31_1","unstructured":"Steffen\u00a0 L Lauritzen . 1996. Graphical models. Vol.\u00a017 . Clarendon Press . Steffen\u00a0L Lauritzen. 1996. Graphical models. Vol.\u00a017. Clarendon Press."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0047-259X(03)00096-4"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jfineco.2015.02.003"},{"key":"e_1_3_2_1_34_1","unstructured":"Y. Li R. Yu C. Shahabi and Y. Liu. 2017. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. arXiv preprint arXiv:1707.01926(2017).  Y. Li R. Yu C. Shahabi and Y. Liu. 2017. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. arXiv preprint arXiv:1707.01926(2017)."},{"key":"e_1_3_2_1_35_1","volume-title":"Dynamic graph convolutional networks. Pattern Recognition 97 (Jan","author":"Manessi Franco","year":"2020","unstructured":"Franco Manessi , Alessandro Rozza , and Mario Manzo . 2020. Dynamic graph convolutional networks. Pattern Recognition 97 (Jan . 2020 ), 107000. Franco Manessi, Alessandro Rozza, and Mario Manzo. 2020. Dynamic graph convolutional networks. Pattern Recognition 97 (Jan. 2020), 107000."},{"key":"e_1_3_2_1_36_1","unstructured":"Harry Markowitz. 1959. Portfolio selection.  Harry Markowitz. 1959. Portfolio selection."},{"key":"e_1_3_2_1_37_1","unstructured":"D. Matsunaga T. Suzumura and T. Takahashi. 2019. Exploring Graph Neural Networks for Stock Market Predictions with Rolling Window Analysis. ArXiv (2019).  D. Matsunaga T. Suzumura and T. Takahashi. 2019. Exploring Graph Neural Networks for Stock Market Predictions with Rolling Window Analysis. ArXiv (2019)."},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"crossref","unstructured":"N. Meinshausen and P. B\u00fchlmann. 2006. High-dimensional graphs and variable selection with the lasso. The annals of statistics 34 3 (2006) 1436\u20131462.  N. Meinshausen and P. B\u00fchlmann. 2006. High-dimensional graphs and variable selection with the lasso. The annals of statistics 34 3 (2006) 1436\u20131462.","DOI":"10.1214\/009053606000000281"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10107-012-0629-5"},{"key":"e_1_3_2_1_40_1","first-page":"1","article-title":"High dimensional forecasting via interpretable vector autoregression","volume":"21","author":"Nicholson B","year":"2020","unstructured":"William\u00a0 B Nicholson , Ines Wilms , Jacob Bien , 2020 . High dimensional forecasting via interpretable vector autoregression . JMLR 21 , 166 (2020), 1 \u2013 52 . William\u00a0B Nicholson, Ines Wilms, Jacob Bien, 2020. High dimensional forecasting via interpretable vector autoregression. JMLR 21, 166 (2020), 1\u201352.","journal-title":"JMLR"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1086\/296071"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1198\/jasa.2009.0126"},{"volume-title":"Handbook of macroeconomics. Vol.\u00a02","author":"Stock H.","key":"e_1_3_2_1_43_1","unstructured":"J.\u00a0 H. Stock and M.\u00a0 W. Watson . 2016. Dynamic factor models, factor-augmented vector autoregressions, and structural vector autoregressions in macroeconomics . In Handbook of macroeconomics. Vol.\u00a02 . Elsevier , 415\u2013525. J.\u00a0H. Stock and M.\u00a0W. Watson. 2016. Dynamic factor models, factor-augmented vector autoregressions, and structural vector autoregressions in macroeconomics. In Handbook of macroeconomics. Vol.\u00a02. Elsevier, 415\u2013525."},{"volume-title":"Analysis of financial time series(2. ed. ed.)","author":"Tsay Ruey S.","key":"e_1_3_2_1_44_1","unstructured":"Ruey S. Tsay . 2005. Analysis of financial time series(2. ed. ed.) . Wiley-Interscience , Hoboken, NJ . Ruey S. Tsay. 2005. Analysis of financial time series(2. ed. ed.). Wiley-Interscience, Hoboken, NJ."},{"key":"e_1_3_2_1_45_1","unstructured":"Yuhao Wang Uma Roy and Caroline Uhler. 2020. Learning high-dimensional gaussian graphical models under total positivity without adjustment of tuning parameters. In AISTATS. PMLR 2698\u20132708.  Yuhao Wang Uma Roy and Caroline Uhler. 2020. Learning high-dimensional gaussian graphical models under total positivity without adjustment of tuning parameters. In AISTATS. PMLR 2698\u20132708."},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"crossref","unstructured":"B. Yu H. Yin and Z. Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv (2017).  B. Yu H. Yin and Z. Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv (2017).","DOI":"10.24963\/ijcai.2018\/505"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/asm018"},{"key":"e_1_3_2_1_48_1","unstructured":"L. Zhao Y. Song M. Deng and H. Li. 2018. Temporal Graph Convolutional Network for Urban Traffic Flow Prediction Method. ArXiv abs\/1811.05320(2018).  L. Zhao Y. Song M. Deng and H. Li. 2018. Temporal Graph Convolutional Network for Urban Traffic Flow Prediction Method. ArXiv abs\/1811.05320(2018)."}],"event":{"name":"ICAIF '22: 3rd ACM International Conference on AI in Finance","sponsor":["ACM Association for Computing Machinery"],"location":"New York NY USA","acronym":"ICAIF '22"},"container-title":["Proceedings of the Third ACM International Conference on AI in Finance"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3533271.3561702","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3533271.3561702","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:00:38Z","timestamp":1750186838000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3533271.3561702"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,26]]},"references-count":48,"alternative-id":["10.1145\/3533271.3561702","10.1145\/3533271"],"URL":"https:\/\/doi.org\/10.1145\/3533271.3561702","relation":{},"subject":[],"published":{"date-parts":[[2022,10,26]]},"assertion":[{"value":"2022-10-26","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}