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Transform learning, was originally developed for static problems; we modify it to model dynamical systems by introducing a feedback loop. The learnt transform coefficients for the\n            <jats:italic>t<\/jats:italic>\n            th instant are fed back along with the\n            <jats:italic>t<\/jats:italic>\n            + 1st sample, thereby establishing a Markovian relationship. Furthermore, the formulation is made supervised by the label consistency cost. Our approach keeps the best of two worlds, marrying the interpretability and uncertainty measure of signal processing with the function approximation ability of neural networks. We have carried out experiments on one of the most challenging problems in dynamical modeling - stock forecasting. Benchmarking with the state-of-the-art has shown that our method excels over the rest.\n          <\/jats:p>","DOI":"10.1145\/3447394","type":"journal-article","created":{"date-parts":[[2021,5,10]],"date-time":"2021-05-10T22:24:14Z","timestamp":1620685454000},"page":"1-18","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Sequential Transform Learning"],"prefix":"10.1145","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0787-2755","authenticated-orcid":false,"given":"Shalini","family":"Sharma","sequence":"first","affiliation":[{"name":"Indraprastha Institute of Information Technology-Delhi, Okhla Industrial Estate, Phase III, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Angshul","family":"Majumdar","sequence":"additional","affiliation":[{"name":"Indraprastha Institute of Information Technology-Delhi, Okhla Industrial Estate, Phase III, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,5,10]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF00126626"},{"key":"e_1_2_1_2_1","volume-title":"Proceedings of the 15th International Conference on Computer and Information Science. 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Basel II and developing countries: Diversification and portfolio effects. (2003).  S. Griffith-Jones M. A. Segoviano and S. Spratt. 2003. Basel II and developing countries: Diversification and portfolio effects. (2003)."},{"key":"e_1_2_1_29_1","volume-title":"Adaptive selection of US stocks with neural nets. Trading on the Edge: Neural, Genetic, and Fuzzy Systems for Chaotic Financial Markets","author":"Hall J. W","year":"1994","unstructured":"J. W Hall . 1994. Adaptive selection of US stocks with neural nets. Trading on the Edge: Neural, Genetic, and Fuzzy Systems for Chaotic Financial Markets . New York : Wiley . ( 1994 ), 45--65. J. W Hall. 1994. Adaptive selection of US stocks with neural nets. Trading on the Edge: Neural, Genetic, and Fuzzy Systems for Chaotic Financial Markets. New York: Wiley. 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Forecasting With Exponential Smoothing: The State Space Approach. Springer Science & Business Media.  R. Hyndman A. B. Koehler J. K. Ord and R. D. Snyder. 2008. Forecasting With Exponential Smoothing: The State Space Approach. Springer Science & Business Media.","DOI":"10.1007\/978-3-540-71918-2"},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.5772\/50938"},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1002\/wilm.42820030315"},{"key":"e_1_2_1_38_1","first-page":"149","article-title":"A survey on stock price prediction using deep learning","volume":"6","author":"Kale K.","year":"2020","unstructured":"K. Kale and S. B. Ahire . 2020 . A survey on stock price prediction using deep learning . CLIO An Annual Interdisciplinary Journal of History 6 , 5 (2020), 149 -- 156 . K. Kale and S. B. Ahire. 2020. A survey on stock price prediction using deep learning. 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In Proceedings of the International Joint Conference on Neural Networks. IEEE, 1--8. K. Kashiparekh, J. Narwariya, P. Malhotra, L. Vig, and G. Shroff. 2019. ConvTimeNet: A pre-trained deep convolutional neural network for time series classification. In Proceedings of the International Joint Conference on Neural Networks. IEEE, 1--8."},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2012.09.024"},{"key":"e_1_2_1_42_1","volume-title":"Proceedings of the Advances in Neural Information Processing Systems. 5574--5584","author":"Kendall A.","unstructured":"A. Kendall and Y. Gal . 2017. What uncertainties do we need in Bayesian deep learning for computer vision? . In Proceedings of the Advances in Neural Information Processing Systems. 5574--5584 . A. Kendall and Y. Gal. 2017. What uncertainties do we need in Bayesian deep learning for computer vision?. In Proceedings of the Advances in Neural Information Processing Systems. 5574--5584."},{"key":"e_1_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2015.2468589"},{"key":"e_1_2_1_44_1","volume-title":"Proceedings of the World Congress on Neural Networks. Citeseer, 491--496","author":"Koskela T.","unstructured":"T. Koskela , M. Lehtokangas , J. Saarinen , and K. Kaski . 1996. Time series prediction with multilayer perceptron, FIR and Elman neural networks . In Proceedings of the World Congress on Neural Networks. Citeseer, 491--496 . T. Koskela, M. Lehtokangas, J. Saarinen, and K. Kaski. 1996. Time series prediction with multilayer perceptron, FIR and Elman neural networks. In Proceedings of the World Congress on Neural Networks. Citeseer, 491--496."},{"key":"e_1_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1504\/IJBAAF.2014.064307"},{"key":"e_1_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2007.891629"},{"key":"e_1_2_1_47_1","first-page":"2006","article-title":"Using neural networks to forecast stock market prices","volume":"333","author":"Lawrence R.","year":"1997","unstructured":"R. Lawrence . 1997 . Using neural networks to forecast stock market prices . University of Manitoba 333 (1997), 2006 -- 2013 . R. Lawrence. 1997. Using neural networks to forecast stock market prices. University of Manitoba 333 (1997), 2006--2013.","journal-title":"University of Manitoba"},{"key":"e_1_2_1_48_1","first-page":"53","article-title":"Forecasting Korean stock price index (kospi) using back propagation neural network model, Bayesian chiao\u2019s model, and sarima model","volume":"11","author":"Lee K. J.","year":"2008","unstructured":"K. J. Lee , A. Y. Chi , S. Yoo , and J. J. Jin . 2008 . Forecasting Korean stock price index (kospi) using back propagation neural network model, Bayesian chiao\u2019s model, and sarima model . Journal of Management Information and Decision Sciences 11 , 2 (2008), 53 . K. J. Lee, A. Y. Chi, S. Yoo, and J. J. Jin. 2008. Forecasting Korean stock price index (kospi) using back propagation neural network model, Bayesian chiao\u2019s model, and sarima model. Journal of Management Information and Decision Sciences 11, 2 (2008), 53.","journal-title":"Journal of Management Information and Decision Sciences"},{"key":"e_1_2_1_49_1","doi-asserted-by":"crossref","unstructured":"B. Li S. C. Hoi P. Zhao and V. Gopalkrishnan. 2013. Confidence weighted mean reversion strategy for online portfolio selection. ACM Transactions on Knowledge Discovery from Data 7 1 (2013) 1--38.  B. Li S. C. Hoi P. Zhao and V. Gopalkrishnan. 2013. Confidence weighted mean reversion strategy for online portfolio selection. ACM Transactions on Knowledge Discovery from Data 7 1 (2013) 1--38.","DOI":"10.1145\/2435209.2435213"},{"key":"e_1_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1080\/14445921.2016.1225149"},{"key":"e_1_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.05.028"},{"key":"e_1_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.1979.1101943"},{"key":"e_1_2_1_53_1","first-page":"21","article-title":"Reconciling efficient markets with behavioral finance: the adaptive markets hypothesis","volume":"7","author":"Andrew W. L.","year":"2005","unstructured":"W. L. Andrew . 2005 . Reconciling efficient markets with behavioral finance: the adaptive markets hypothesis . Journal of Investment Consulting 7 , 2 (2005), 21 -- 44 . W. L. Andrew. 2005. Reconciling efficient markets with behavioral finance: the adaptive markets hypothesis. Journal of Investment Consulting 7, 2 (2005), 21--44.","journal-title":"Journal of Investment Consulting"},{"key":"e_1_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2018.10.034"},{"key":"e_1_2_1_55_1","volume-title":"Proceedings of the Advances in Neural Information Processing Systems. 13153--13164","author":"Maddox W. J.","unstructured":"W. J. Maddox , P. Izmailov , T. Garipov , D. P. Vetrov , and A. G. Wilson . 2019. A simple baseline for Bayesian uncertainty in deep learning . In Proceedings of the Advances in Neural Information Processing Systems. 13153--13164 . W. J. Maddox, P. Izmailov, T. Garipov, D. P. Vetrov, and A. G. Wilson. 2019. A simple baseline for Bayesian uncertainty in deep learning. In Proceedings of the Advances in Neural Information Processing Systems. 13153--13164."},{"key":"e_1_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2019.2899121"},{"key":"e_1_2_1_57_1","volume-title":"Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 6782--6786","author":"Maggu J.","unstructured":"J. Maggu and A. Majumdar . 2018. Unsupervised deep transform learning . In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 6782--6786 . J. Maggu and A. Majumdar. 2018. Unsupervised deep transform learning. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 6782--6786."},{"key":"e_1_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1540-6261.1970.tb00518.x"},{"key":"e_1_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.5121\/ijcsea.2014.4202"},{"key":"e_1_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2017.7966019"},{"key":"e_1_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2012.10.005"},{"key":"e_1_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1016\/0377-2217(91)90190-7"},{"key":"e_1_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.omega.2004.07.024"},{"key":"e_1_2_1_64_1","volume-title":"Forecasting With Dynamic Regression Models","author":"Pankratz A.","unstructured":"A. Pankratz . 2012. Forecasting With Dynamic Regression Models . Vol. 935 . John Wiley & Sons . A. Pankratz. 2012. Forecasting With Dynamic Regression Models. Vol. 935. John Wiley & Sons."},{"key":"e_1_2_1_65_1","unstructured":"A. C. Petricua S. Stancu and A. Tindeche. 2016. Limitation of ARIMA models in financial and monetary economics.Theoretical & Applied Economics 23 4 (2016).  A. C. Petricua S. Stancu and A. Tindeche. 2016. Limitation of ARIMA models in financial and monetary economics.Theoretical & Applied Economics 23 4 (2016)."},{"key":"e_1_2_1_66_1","doi-asserted-by":"crossref","unstructured":"T. Rakthanmanon B. Campana A. Mueen G. Batista B. Westover Q. Zhu J. Zakaria and E. Keogh. 2013. Addressing big data time series: Mining trillions of time series subsequences under dynamic time warping. ACM Transactions on Knowledge Discovery from Data 7 3 (2013) 1--31.  T. Rakthanmanon B. Campana A. Mueen G. Batista B. Westover Q. Zhu J. Zakaria and E. Keogh. 2013. Addressing big data time series: Mining trillions of time series subsequences under dynamic time warping. ACM Transactions on Knowledge Discovery from Data 7 3 (2013) 1--31.","DOI":"10.1145\/2513092.2500489"},{"key":"e_1_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2012.2226449"},{"key":"e_1_2_1_68_1","first-page":"1754","article-title":"Predicting stock prices using LSTM","volume":"6","author":"Roondiwala M.","year":"2017","unstructured":"M. Roondiwala , H. Patel , and S. Varma . 2017 . Predicting stock prices using LSTM . International Journal of Science and Research 6 , 4 (2017), 1754 -- 1756 . M. Roondiwala, H. Patel, and S. Varma. 2017. Predicting stock prices using LSTM. International Journal of Science and Research 6, 4 (2017), 1754--1756.","journal-title":"International Journal of Science and Research"},{"key":"e_1_2_1_69_1","doi-asserted-by":"crossref","unstructured":"D. E. Rumelhart G. E. Hinton and R. J. Williams. 1986. Learning Internal Representations by Error Propagation. MIT Press Cambridge MA USA 318--362.  D. E. Rumelhart G. E. Hinton and R. J. Williams. 1986. Learning Internal Representations by Error Propagation. MIT Press Cambridge MA USA 318--362.","DOI":"10.21236\/ADA164453"},{"key":"e_1_2_1_70_1","first-page":"115","article-title":"Forecasting volatility in indian stock market using State Space models","volume":"3","author":"Saini N.","year":"2014","unstructured":"N. Saini , A. K. Mittal , et\u00a0al. 2014 . Forecasting volatility in indian stock market using State Space models . Journal of Statistical and Econometric Methods 3 , 1 (2014), 115 -- 136 . N. Saini, A. K. Mittal, et\u00a0al. 2014. Forecasting volatility in indian stock market using State Space models. Journal of Statistical and Econometric Methods 3, 1 (2014), 115--136.","journal-title":"Journal of Statistical and Econometric Methods"},{"key":"e_1_2_1_71_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICIINFS.2017.8300345"},{"key":"e_1_2_1_72_1","volume-title":"Bayesian Filtering and Smoothing (3 ed.)","author":"Sarkka S.","unstructured":"S. Sarkka . 2013. Bayesian Filtering and Smoothing (3 ed.) . Cambridge University Press . S. Sarkka. 2013. Bayesian Filtering and Smoothing (3 ed.). Cambridge University Press."},{"key":"e_1_2_1_73_1","doi-asserted-by":"publisher","DOI":"10.1109\/78.650093"},{"key":"e_1_2_1_74_1","doi-asserted-by":"crossref","unstructured":"O. B. Sezer and A. M. Ozbayoglu. 2018. Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Algorithmic Financial Trading with Deep Convolutional Neural Networks: Time Series to Image Conversion Approach 70 (2018) 525--538.  O. B. Sezer and A. M. Ozbayoglu. 2018. Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. 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World Academy of Science, Engineering and Technology 22 (2006), 134--139.","journal-title":"World Academy of Science, Engineering and Technology"},{"key":"e_1_2_1_79_1","unstructured":"D. Smirnov and E. M. Nguifo. 2018. Time series classification with recurrent neural networks. Advanced Analytics and Learning on Temporal Data 8 (2018).  D. Smirnov and E. M. Nguifo. 2018. Time series classification with recurrent neural networks. Advanced Analytics and Learning on Temporal Data 8 (2018)."},{"key":"e_1_2_1_80_1","doi-asserted-by":"publisher","DOI":"10.1198\/073500105000000199"},{"key":"e_1_2_1_81_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2007.07.008"},{"key":"e_1_2_1_82_1","volume-title":"Proceedings of the IEEE Adaptive Systems for Signal Processing, Communications, and Control Symposium. IEEE, 153--158","author":"E.","unstructured":"E. A Wan and R. Van Der Merwe. 2000. The unscented kalman filter for nonlinear estimation . 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In Proceedings of the 36th Chinese Control Conference. IEEE, 3882--3887 . B. Yang, Z. J. Gong, and W. Yang. 2017. Stock market index prediction using deep neural network ensemble. In Proceedings of the 36th Chinese Control Conference. IEEE, 3882--3887."},{"key":"e_1_2_1_85_1","volume-title":"IOP Conference Series: Materials Science and Engineering","volume":"569","author":"Zhang X.","year":"2037","unstructured":"X. Zhang , X. Liang , A. Zhiyuli , S. Zhang , R. Xu , and B. Wu . 2019. AT-LSTM: An attention-based LSTM model for financial time series prediction . In IOP Conference Series: Materials Science and Engineering , Vol. 569 . IOP Publishing, 05 2037 . X. Zhang, X. Liang, A. Zhiyuli, S. Zhang, R. Xu, and B. Wu. 2019. AT-LSTM: An attention-based LSTM model for financial time series prediction. In IOP Conference Series: Materials Science and Engineering, Vol. 569. 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