Generación de series de tiempo financieras sintéticas para "data augmentation" usando redes neuronales generativas adversarias (GAN)

Los modelos GAN se han usado de forma exitosa para realizar aumento de datos en problemas relacionados con imágenes, audio y video, pues logran representar adecuadamente las propiedades de los datos reales, pero incorporando suficiente diversidad en los datos sintéticos generados como para poder mej...

Full description

Autores:: Villarraga Ossa, Edwin Fernando

Tipo de recurso:

Fecha de publicación:: 2021

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: spa

id	UNACIONAL2_b6f2a7dd1023cc5388ee0a2692d7e9a5
oai_identifier_str	oai:repositorio.unal.edu.co:unal/79374
network_acronym_str	UNACIONAL2
network_name_str	Universidad Nacional de Colombia
repository_id_str
dc.title.spa.fl_str_mv	Generación de series de tiempo financieras sintéticas para "data augmentation" usando redes neuronales generativas adversarias (GAN)
dc.title.translated.none.fl_str_mv	Generation of synthetic financial time series for "data augmentation" using generative adverdarial networks (GAN)
title	Generación de series de tiempo financieras sintéticas para "data augmentation" usando redes neuronales generativas adversarias (GAN)
spellingShingle	Generación de series de tiempo financieras sintéticas para "data augmentation" usando redes neuronales generativas adversarias (GAN) 000 - Ciencias de la computación, información y obras generales::006 - Métodos especiales de computación Finanzas - Modelos estocásticos Análisis de series de tiempo Análisis estocástico Redes Neuronales Simulación Modelo generativo GAN Data Augmentation Overfitting Deep Learning
title_short	Generación de series de tiempo financieras sintéticas para "data augmentation" usando redes neuronales generativas adversarias (GAN)
title_full	Generación de series de tiempo financieras sintéticas para "data augmentation" usando redes neuronales generativas adversarias (GAN)
title_fullStr	Generación de series de tiempo financieras sintéticas para "data augmentation" usando redes neuronales generativas adversarias (GAN)
title_full_unstemmed	Generación de series de tiempo financieras sintéticas para "data augmentation" usando redes neuronales generativas adversarias (GAN)
title_sort	Generación de series de tiempo financieras sintéticas para "data augmentation" usando redes neuronales generativas adversarias (GAN)
dc.creator.fl_str_mv	Villarraga Ossa, Edwin Fernando
dc.contributor.advisor.none.fl_str_mv	Villa Garzón, Fernán Alonso
dc.contributor.author.none.fl_str_mv	Villarraga Ossa, Edwin Fernando
dc.subject.ddc.spa.fl_str_mv	000 - Ciencias de la computación, información y obras generales::006 - Métodos especiales de computación
topic	000 - Ciencias de la computación, información y obras generales::006 - Métodos especiales de computación Finanzas - Modelos estocásticos Análisis de series de tiempo Análisis estocástico Redes Neuronales Simulación Modelo generativo GAN Data Augmentation Overfitting Deep Learning
dc.subject.lemb.none.fl_str_mv	Finanzas - Modelos estocásticos Análisis de series de tiempo Análisis estocástico
dc.subject.proposal.spa.fl_str_mv	Redes Neuronales Simulación Modelo generativo
dc.subject.proposal.eng.fl_str_mv	GAN Data Augmentation Overfitting Deep Learning
description	Los modelos GAN se han usado de forma exitosa para realizar aumento de datos en problemas relacionados con imágenes, audio y video, pues logran representar adecuadamente las propiedades de los datos reales, pero incorporando suficiente diversidad en los datos sintéticos generados como para poder mejorar el desempeño de los modelos de machine learning y deep learning en las evaluaciones por fuera de muestra. Las series de tiempo financieras se requieren para la modelación y solución de problemas en finanzas, sin embargo, dada la escasez de datos históricos, no solo originados por problemas de recolección de datos, sino también porque una serie de tiempo es solamente la realización de un proceso estocástico y por ende se presenta un sub muestreo. En este trabajo se generaron series de tiempo sintéticas usando DCGAN y cCGAN para generar datos de rendimientos, volúmenes, bid-ask spread, y precios con transformación fraccional, de acciones de Estados Unidos de América, con periodicidad diaria e intradiaria. Se pudo verificar que estos modelos GAN logran generar series simuladas que representan adecuadamente las propiedades distribucionales de las series históricas. Estas series sintéticas generadas pueden servir como insumo del tipo data augmentation en modelos de machine learning y deep learning para mejorar su desempeño con datos por fuera de muestra.
publishDate	2021
dc.date.accessioned.none.fl_str_mv	2021-03-25T20:36:10Z
dc.date.available.none.fl_str_mv	2021-03-25T20:36:10Z
dc.date.issued.none.fl_str_mv	2021-03-24
dc.type.spa.fl_str_mv	Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv	Text
dc.type.redcol.spa.fl_str_mv	http://purl.org/redcol/resource_type/TM
status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/79374
dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/79374 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	spa
language	spa
dc.relation.references.spa.fl_str_mv	1. Sezer OB, Gudelek MU, Ozbayoglu AM. Financial time series forecasting with deep learning : A systematic literature review: 2005–2019. Applied Soft Computing. 2020. p. 106181. doi:10.1016/j.asoc.2020.106181 2. Kakushadze Z, Serur JA. 151 Trading Strategies. 2018. doi:10.1007/978-3-030-02792-6 3. Brooks C, Hoepner AGF, McMillan DG, Vivian A, Simen CW. Financial Data Science: The Birth of a New Financial Research Paradigm Complementing Econometrics? SSRN Electronic Journal. doi:10.2139/ssrn.3580729 4. White H. A Reality Check for Data Snooping. Econometrica. 2000. pp. 1097–1126. doi:10.1111/1468-0262.00152 5. Prado ML de, de Prado ML. Advances in Financial Machine Learning: Lecture 3/10. SSRN Electronic Journal. doi:10.2139/ssrn.3257419 6. Gooijer JGD, De Gooijer JG, Hyndman RJ. 25 years of time series forecasting. International Journal of Forecasting. 2006. pp. 443–473. doi:10.1016/j.ijforecast.2006.01.001 7. Box GEP, Jenkins GM. Time Series Analysis: Forecasting and Control, Revised Ed. 1976. 8. Cont R. Empirical properties of asset returns: stylized facts and statistical issues. Quant Finance. 2001;1: 223–236. 9. Tsay RS. Analysis of Financial Time Series. Wiley Series in Probability and Statistics. 2005. doi:10.1002/0471746193 10. Zhou X, Pan Z, Hu G, Tang S, Zhao C. Stock Market Prediction on High-Frequency Data Using Generative Adversarial Nets. Mathematical Problems in Engineering. 2018. pp. 1–11. doi:10.1155/2018/4907423 11. Dacorogna M et al. An Introduction to High-Frequency Finance. 2001. doi:10.1016/b978-0-12-279671-5.x5000-x 12. Rydberg TH. Realistic Statistical Modelling of Financial Data. Int Stat Rev. 2000;68: 233–258. 13. Cartea Á, Jaimungal S, Ricci J. Algorithmic Trading, Stochastic Control, and Mutually Exciting Processes. SIAM Review. 2018. pp. 673–703. doi:10.1137/18m1176968 14. Cartea Á, Jaimungal S. Modeling Asset Prices for Algorithmic and High Frequency Trading. SSRN Electronic Journal. doi:10.2139/ssrn.1722202 15. Agudelo DA, Giraldo S, Villarraga E. Does PIN measure information? Informed trading effects on returns and liquidity in six emerging markets. International Review of Economics & Finance. 2015. pp. 149–161. oi:10.1016/j.iref.2015.04.00266 16. Glosten LR, Milgrom PR. Bid, ask and transaction prices in a specialist market with heterogeneously informed traders. Journal of Financial Economics. 1985. pp. 71–100. doi:10.1016/0304-405x(85)90044-3 17. Kyle AS. Continuous Auctions and Insider Trading. Econometrica. 1985. p. 1315. doi:10.2307/1913210 18. Aldridge I. High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems. John Wiley and Sons; 2009. 19. Lee CMC, Ready MJ. Inferring Trade Direction from Intraday Data. The Journal of Finance. 1991. pp. 733–746. doi:10.1111/j.1540-6261.1991.tb02683.x 20. Easley D, de Prado MML, O’Hara M. Flow Toxicity and Liquidity in a High-frequency World. Review of Financial Studies. 2012. pp. 1457–1493. doi:10.1093/rfs/hhs053 21. Vanstone B, Hahn T. Data Characteristics for High-Frequency Trading Systems. The Handbook of High Frequency Trading. 2015. pp. 47–57. doi:10.1016/b978-0-12-802205-4.00003-8 22. Sirignano J, Cont R. Universal Features of Price Formation in Financial Markets: Perspectives From Deep Learning. SSRN Electronic Journal. doi:10.2139/ssrn.3141294 23. Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, et al. Generative Adversarial Networks. arXiv [stat.ML]. 2014. Available: http://arxiv.org/abs/1406.2661 24. Creswell A, White T, Dumoulin V, Arulkumaran K, Sengupta B, Bharath AA. Generative Adversarial Networks: An Overview. IEEE Signal Processing Magazine. 2018. pp.53–65. doi:10.1109/msp.2017.2765202 25. Donahue J, Krähenbühl P, Darrell T. Adversarial Feature Learning. arXiv [cs.LG]. 2016. Available: http://arxiv.org/abs/1605.09782 26. Salehi P, Chalechale A, Taghizadeh M. Generative Adversarial Networks (GANs): An Overview of Theoretical Model, Evaluation Metrics, and Recent Developments. arXiv [cs.CV]. 2020. Available: ttp://arxiv.org/abs/2005.13178 27. Radford A, Metz L, Chintala S. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. arXiv [cs.LG]. 2015. Available: http://arxiv.org/abs/1511.06434 28. Mirza M, Osindero S. Conditional Generative Adversarial Nets. arXiv [cs.LG]. 2014. Available: http://arxiv.org/abs/1411.1784 29. Chen X, Duan Y, Houthooft R, Schulman J, Sutskever I, Abbeel P. InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets. arXiv [cs.LG]. 2016. Available: http://arxiv.org/abs/1606.03657 30. Odena A, Olah C, Shlens J. Conditional Image Synthesis With Auxiliary Classifier GANs. arXiv [stat.ML]. 2016. Available: http://arxiv.org/abs/1610.09585 31. Odena A. Semi-Supervised Learning with Generative Adversarial Networks. arXiv [stat.ML]. 2016. Available: http://arxiv.org/abs/1606.0158367 32. Brownlee J. Generative Adversarial Networks with Python: Deep Learning Generative Models for Image Synthesis and Image Translation. Machine Learning Mastery; 2019. 33. Makhzani A, Shlens J, Jaitly N, Goodfellow I, Frey B. Adversarial Autoencoders. arXiv [cs.LG]. 2015. Available: http://arxiv.org/abs/1511.05644 34. Dumoulin V, Belghazi I, Poole B, Mastropietro O, Lamb A, Arjovsky M, et al. Adversarially Learned Inference. arXiv [stat.ML]. 2016. Available: http://arxiv.org/abs/1606.00704 35. Larsen ABL, Sønderby SK, Larochelle H, Winther O. Autoencoding beyond pixels using a learned similarity metric. arXiv [cs.LG]. 2015. Available: http://arxiv.org/abs/1512.09300 36. Metz L, Poole B, Pfau D, Sohl-Dickstein J. Unrolled Generative Adversarial Networks. arXiv [cs.LG]. 2016. Available: http://arxiv.org/abs/1611.02163 37. Arjovsky M, Chintala S, Bottou L. Wasserstein Generative Adversarial Networks. In: Precup D, Teh YW, editors. International Convention Centre, Sydney, Australia: PMLR; 2017. pp. 214–223. 38. Petzka H, Fischer A, Lukovnicov D. On the regularization of Wasserstein GANs. arXiv [stat.ML]. 2017. Available: http://arxiv.org/abs/1709.08894 39. Zhu J-Y, Park T, Isola P, Efros AA. Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks. arXiv [cs.CV]. 2017. Available:http://arxiv.org/abs/1703.10593 40. Isola P, Zhu J-Y, Zhou T, Efros AA. Image-to-Image Translation with Conditional Adversarial Networks. arXiv [cs.CV]. 2016. Available: http://arxiv.org/abs/1611.07004 41. Ledig C, Theis L, Huszar F, Caballero J, Cunningham A, Acosta A, et al. Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network. arXiv [cs.CV]. 2016. Available: http://arxiv.org/abs/1609.04802 42. Wang X, Yu K, Wu S, Gu J, Liu Y, Dong C, et al. ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks. arXiv [cs.CV]. 2018. Available: http://arxiv.org/abs/1809.00219 43. Such FP, Rawal A, Lehman J, Stanley KO, Clune J. Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data. arXiv [cs.LG]. 2019. Available: http://arxiv.org/abs/1912.07768 44. Borji A. Pros and Cons of GAN Evaluation Measures. arXiv [cs.CV]. 2018. Available: http://arxiv.org/abs/1802.03446 45. Salimans T, Goodfellow I, Zaremba W, Cheung V, Radford A, Chen X, et al. Improved Techniques for Training GANs. In: Lee DD, Sugiyama M, Luxburg UV, Guyon I, Garnett R, editors. Advances in Neural Information Processing Systems 29. Curran Associates,Inc.; 2016. pp. 2234–2242. 46. Heusel M, Ramsauer H, Unterthiner T, Nessler B, Hochreiter S. GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium. arXiv [cs.LG]. 2017. Available: http://arxiv.org/abs/1706.0850068 47. Frid-Adar M, Klang E, Amitai M, Goldberger J, Greenspan H. Synthetic Data Augmentation using GAN for Improved Liver Lesion Classification. arXiv [cs.CV]. 2018. Available: http://arxiv.org/abs/1801.02385 48. Shrivastava A, Pfister T, Tuzel O, Susskind J, Wang W, Webb R. Learning from Simulated and Unsupervised Images through Adversarial Training. arXiv [cs.CV]. 2016. Available: http://arxiv.org/abs/1612.07828 49. Antoniou A, Storkey A, Edwards H. Data Augmentation Generative Adversarial Networks. arXiv [stat.ML]. 2017. Available: http://arxiv.org/abs/1711.04340 50. Motamed S, Khalvati F. Inception Augmentation Generative Adversarial Network. arXiv [cs.CV]. 2020. Available: http://arxiv.org/abs/2006.03622 51. Lee H, Kim J, Kim EK, Kim S. Wasserstein Generative Adversarial Networks Based Data Augmentation for Radar Data Analysis. NATO Adv Sci Inst Ser E Appl Sci. 2020;10: 1449 52. Zhang K, Zhong G, Dong J, Wang S, Wang Y. Stock Market Prediction Based on Generative Adversarial Network. Procedia Comput Sci. 2019;147: 400–406. 53. Koshiyama A, Firoozye N, Treleaven P. Generative Adversarial Networks for Financial Trading Strategies Fine-Tuning and Combination. arXiv [cs.LG]. 2019. Available: http://arxiv.org/abs/1901.01751 54. Wiese M, Knobloch R, Korn R, Kretschmer P. Quant GANs: deep generation of financial time series. Quantitative Finance. 2020. pp. 1–22. doi:10.1080/14697688.2020.1730426 55. Yoon J, Jarrett D, van der Schaar M. Time-series Generative Adversarial Networks. In:Wallach H, Larochelle H, Beygelzimer A, d\textquotesingle Alché-Buc F, Fox E, Garnett R, editors. Advances in Neural Information Processing Systems 32. Curran Associates,Inc.; 2019. pp. 5508–5518. 56. Esteban C, Hyland SL, Rätsch G. Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs. arXiv [stat.ML]. 2017. Available:http://arxiv.org/abs/1706.02633 57. Hartmann KG, Schirrmeister RT, Ball T. EEG-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signals. arXiv [eess.SP]. 2018. Available: http://arxiv.org/abs/1806.01875 58. Wiese M, Bai L, Wood B, Buehler H. Deep Hedging: Learning to Simulate Equity Option Markets. arXiv [q-fin.CP]. 2019. Available: http://arxiv.org/abs/1911.01700 59. Takahashi S, Chen Y, Tanaka-Ishii K. Modeling financial time-series with generative adversarial networks. Physica A: Statistical Mechanics and its Applications. 2019;527:121261. 60. Guo Z, Wan Y, Ye H. A data imputation method for multivariate time series based on generative adversarial network. Neurocomputing. 2019;360: 185–197. 61. Marti G. CORRGAN: Sampling Realistic Financial Correlation Matrices Using Generative Adversarial Networks. ICASSP 2020 - 2020 IEEE International Conferenceon Acoustics, Speech and Signal Processing (ICASSP). 2020.69 doi:10.1109/icassp40776.2020.9053276 62. Li D, Chen D, Jin B, Shi L, Goh J, Ng S-K. MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks. Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time Series. 2019. pp. 703–716.doi:10.1007/978-3-030-30490-4_56
dc.rights.spa.fl_str_mv	Derechos reservados - Universidad Nacional de Colombia
dc.rights.coar.fl_str_mv	http://purl.org/coar/access_right/c_abf2
dc.rights.license.spa.fl_str_mv	Atribución-NoComercial-SinDerivadas 4.0 Internacional
dc.rights.uri.spa.fl_str_mv	http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.accessrights.spa.fl_str_mv	info:eu-repo/semantics/openAccess
rights_invalid_str_mv	Atribución-NoComercial-SinDerivadas 4.0 Internacional Derechos reservados - Universidad Nacional de Colombia http://creativecommons.org/licenses/by-nc-nd/4.0/ http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv	openAccess
dc.format.extent.spa.fl_str_mv	76 páginas
dc.format.mimetype.spa.fl_str_mv	application/pdf
dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv	Medellín - Minas - Maestría en Ingeniería - Analítica
dc.publisher.faculty.spa.fl_str_mv	Minas
dc.publisher.place.spa.fl_str_mv	Medellín
dc.publisher.branch.spa.fl_str_mv	Universidad Nacional de Colombia - Sede Medellín
institution	Universidad Nacional de Colombia
bitstream.url.fl_str_mv	https://repositorio.unal.edu.co/bitstream/unal/79374/4/71783907.2021.pdf https://repositorio.unal.edu.co/bitstream/unal/79374/5/license.txt https://repositorio.unal.edu.co/bitstream/unal/79374/6/license_rdf https://repositorio.unal.edu.co/bitstream/unal/79374/7/71783907.2021.pdf.jpg
bitstream.checksum.fl_str_mv	9ae5e37d6e48c21e8f3b197eb4118569 cccfe52f796b7c63423298c2d3365fc6 84a900c9dd4b2a10095a94649e1ce116 6383cd9c985fd051199afddf80fe6aa1
bitstream.checksumAlgorithm.fl_str_mv	MD5 MD5 MD5 MD5
repository.name.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
repository.mail.fl_str_mv	repositorio_nal@unal.edu.co
_version_	1814089770059431936
spelling	Atribución-NoComercial-SinDerivadas 4.0 InternacionalDerechos reservados - Universidad Nacional de Colombiahttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Villa Garzón, Fernán Alonso9c83ea56495b8f17a79c27fd0001bb81Villarraga Ossa, Edwin Fernando4e83e2dfb10c299f3e8752c16196e27a2021-03-25T20:36:10Z2021-03-25T20:36:10Z2021-03-24https://repositorio.unal.edu.co/handle/unal/79374Universidad Nacional de ColombiaRepositorio Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/Los modelos GAN se han usado de forma exitosa para realizar aumento de datos en problemas relacionados con imágenes, audio y video, pues logran representar adecuadamente las propiedades de los datos reales, pero incorporando suficiente diversidad en los datos sintéticos generados como para poder mejorar el desempeño de los modelos de machine learning y deep learning en las evaluaciones por fuera de muestra. Las series de tiempo financieras se requieren para la modelación y solución de problemas en finanzas, sin embargo, dada la escasez de datos históricos, no solo originados por problemas de recolección de datos, sino también porque una serie de tiempo es solamente la realización de un proceso estocástico y por ende se presenta un sub muestreo. En este trabajo se generaron series de tiempo sintéticas usando DCGAN y cCGAN para generar datos de rendimientos, volúmenes, bid-ask spread, y precios con transformación fraccional, de acciones de Estados Unidos de América, con periodicidad diaria e intradiaria. Se pudo verificar que estos modelos GAN logran generar series simuladas que representan adecuadamente las propiedades distribucionales de las series históricas. Estas series sintéticas generadas pueden servir como insumo del tipo data augmentation en modelos de machine learning y deep learning para mejorar su desempeño con datos por fuera de muestra.GAN models have been used successfully as a data augmentation method applied to problems related to images, audio and video, since they manage to adequately represent the properties of the real data, but incorporating diversity in the synthetic data generated in order to improve the out-of-sample performance of Machine Learning and Deep Learning models. Financial time series are required for modeling and solving problems in finance, however, given the scarcity of historical data, not only caused by data collection problems, but also because a time series is the realization of only one stochastic process and therefore a subsampling is presented. In this work, synthetic time series were generated using DCGAN and cCGAN to generate data on yields, volumes, bid-ask spread, and prices with fractional transformation, of shares of the United States of America, with daily and intraday periodicity. It was possible to verify that these GAN models manage to generate simulated series that adequately represent the distributional properties of the historical time series. These generated synthetic time series can serve as data augmentation to machine learning and deep learning models to improve their out-of-sample performance.Maestría76 páginasapplication/pdfspaUniversidad Nacional de ColombiaMedellín - Minas - Maestría en Ingeniería - AnalíticaMinasMedellínUniversidad Nacional de Colombia - Sede Medellín000 - Ciencias de la computación, información y obras generales::006 - Métodos especiales de computaciónFinanzas - Modelos estocásticosAnálisis de series de tiempoAnálisis estocásticoRedes NeuronalesSimulaciónModelo generativoGANData AugmentationOverfittingDeep LearningGeneración de series de tiempo financieras sintéticas para "data augmentation" usando redes neuronales generativas adversarias (GAN)Generation of synthetic financial time series for "data augmentation" using generative adverdarial networks (GAN)Trabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TM1. Sezer OB, Gudelek MU, Ozbayoglu AM. Financial time series forecasting with deep learning : A systematic literature review: 2005–2019. Applied Soft Computing. 2020. p. 106181. doi:10.1016/j.asoc.2020.1061812. Kakushadze Z, Serur JA. 151 Trading Strategies. 2018. doi:10.1007/978-3-030-02792-6 3. Brooks C, Hoepner AGF, McMillan DG, Vivian A, Simen CW. Financial Data Science: The Birth of a New Financial Research Paradigm Complementing Econometrics? SSRN Electronic Journal. doi:10.2139/ssrn.35807294. White H. A Reality Check for Data Snooping. Econometrica. 2000. pp. 1097–1126. doi:10.1111/1468-0262.001525. Prado ML de, de Prado ML. Advances in Financial Machine Learning: Lecture 3/10. SSRN Electronic Journal. doi:10.2139/ssrn.32574196. Gooijer JGD, De Gooijer JG, Hyndman RJ. 25 years of time series forecasting. International Journal of Forecasting. 2006. pp. 443–473. doi:10.1016/j.ijforecast.2006.01.0017. Box GEP, Jenkins GM. Time Series Analysis: Forecasting and Control, Revised Ed. 1976.8. Cont R. Empirical properties of asset returns: stylized facts and statistical issues. Quant Finance. 2001;1: 223–236.9. Tsay RS. Analysis of Financial Time Series. Wiley Series in Probability and Statistics. 2005. doi:10.1002/047174619310. Zhou X, Pan Z, Hu G, Tang S, Zhao C. Stock Market Prediction on High-Frequency Data Using Generative Adversarial Nets. Mathematical Problems in Engineering. 2018. pp. 1–11. doi:10.1155/2018/490742311. Dacorogna M et al. An Introduction to High-Frequency Finance. 2001. doi:10.1016/b978-0-12-279671-5.x5000-x12. Rydberg TH. Realistic Statistical Modelling of Financial Data. Int Stat Rev. 2000;68: 233–258.13. Cartea Á, Jaimungal S, Ricci J. Algorithmic Trading, Stochastic Control, and Mutually Exciting Processes. SIAM Review. 2018. pp. 673–703. doi:10.1137/18m117696814. Cartea Á, Jaimungal S. Modeling Asset Prices for Algorithmic and High Frequency Trading. SSRN Electronic Journal. doi:10.2139/ssrn.172220215. Agudelo DA, Giraldo S, Villarraga E. Does PIN measure information? Informed trading effects on returns and liquidity in six emerging markets. International Review of Economics & Finance. 2015. pp. 149–161. oi:10.1016/j.iref.2015.04.0026616. Glosten LR, Milgrom PR. Bid, ask and transaction prices in a specialist market with heterogeneously informed traders. Journal of Financial Economics. 1985. pp. 71–100. doi:10.1016/0304-405x(85)90044-317. Kyle AS. Continuous Auctions and Insider Trading. Econometrica. 1985. p. 1315. doi:10.2307/191321018. Aldridge I. High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems. John Wiley and Sons; 2009.19. Lee CMC, Ready MJ. Inferring Trade Direction from Intraday Data. The Journal of Finance. 1991. pp. 733–746. doi:10.1111/j.1540-6261.1991.tb02683.x20. Easley D, de Prado MML, O’Hara M. Flow Toxicity and Liquidity in a High-frequency World. Review of Financial Studies. 2012. pp. 1457–1493. doi:10.1093/rfs/hhs05321. Vanstone B, Hahn T. Data Characteristics for High-Frequency Trading Systems. The Handbook of High Frequency Trading. 2015. pp. 47–57. doi:10.1016/b978-0-12-802205-4.00003-822. Sirignano J, Cont R. Universal Features of Price Formation in Financial Markets: Perspectives From Deep Learning. SSRN Electronic Journal. doi:10.2139/ssrn.314129423. Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, et al. Generative Adversarial Networks. arXiv [stat.ML]. 2014. Available: http://arxiv.org/abs/1406.266124. Creswell A, White T, Dumoulin V, Arulkumaran K, Sengupta B, Bharath AA. Generative Adversarial Networks: An Overview. IEEE Signal Processing Magazine. 2018. pp.53–65. doi:10.1109/msp.2017.276520225. Donahue J, Krähenbühl P, Darrell T. Adversarial Feature Learning. arXiv [cs.LG]. 2016. Available: http://arxiv.org/abs/1605.0978226. Salehi P, Chalechale A, Taghizadeh M. Generative Adversarial Networks (GANs): An Overview of Theoretical Model, Evaluation Metrics, and Recent Developments. arXiv [cs.CV]. 2020. Available: ttp://arxiv.org/abs/2005.1317827. Radford A, Metz L, Chintala S. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. arXiv [cs.LG]. 2015. Available: http://arxiv.org/abs/1511.0643428. Mirza M, Osindero S. Conditional Generative Adversarial Nets. arXiv [cs.LG]. 2014. Available: http://arxiv.org/abs/1411.178429. Chen X, Duan Y, Houthooft R, Schulman J, Sutskever I, Abbeel P. InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets. arXiv [cs.LG]. 2016. Available: http://arxiv.org/abs/1606.0365730. Odena A, Olah C, Shlens J. Conditional Image Synthesis With Auxiliary Classifier GANs. arXiv [stat.ML]. 2016. Available: http://arxiv.org/abs/1610.0958531. Odena A. Semi-Supervised Learning with Generative Adversarial Networks. arXiv [stat.ML]. 2016. Available: http://arxiv.org/abs/1606.015836732. Brownlee J. Generative Adversarial Networks with Python: Deep Learning Generative Models for Image Synthesis and Image Translation. Machine Learning Mastery; 2019.33. Makhzani A, Shlens J, Jaitly N, Goodfellow I, Frey B. Adversarial Autoencoders. arXiv [cs.LG]. 2015. Available: http://arxiv.org/abs/1511.05644 34. Dumoulin V, Belghazi I, Poole B, Mastropietro O, Lamb A, Arjovsky M, et al. Adversarially Learned Inference. arXiv [stat.ML]. 2016. Available: http://arxiv.org/abs/1606.0070435. Larsen ABL, Sønderby SK, Larochelle H, Winther O. Autoencoding beyond pixels using a learned similarity metric. arXiv [cs.LG]. 2015. Available: http://arxiv.org/abs/1512.0930036. Metz L, Poole B, Pfau D, Sohl-Dickstein J. Unrolled Generative Adversarial Networks. arXiv [cs.LG]. 2016. Available: http://arxiv.org/abs/1611.0216337. Arjovsky M, Chintala S, Bottou L. Wasserstein Generative Adversarial Networks. In: Precup D, Teh YW, editors. International Convention Centre, Sydney, Australia: PMLR; 2017. pp. 214–223.38. Petzka H, Fischer A, Lukovnicov D. On the regularization of Wasserstein GANs. arXiv [stat.ML]. 2017. Available: http://arxiv.org/abs/1709.0889439. Zhu J-Y, Park T, Isola P, Efros AA. Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks. arXiv [cs.CV]. 2017. Available:http://arxiv.org/abs/1703.1059340. Isola P, Zhu J-Y, Zhou T, Efros AA. Image-to-Image Translation with Conditional Adversarial Networks. arXiv [cs.CV]. 2016. Available: http://arxiv.org/abs/1611.07004 41. Ledig C, Theis L, Huszar F, Caballero J, Cunningham A, Acosta A, et al. Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network. arXiv [cs.CV]. 2016. Available: http://arxiv.org/abs/1609.0480242. Wang X, Yu K, Wu S, Gu J, Liu Y, Dong C, et al. ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks. arXiv [cs.CV]. 2018. Available: http://arxiv.org/abs/1809.0021943. Such FP, Rawal A, Lehman J, Stanley KO, Clune J. Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data. arXiv [cs.LG]. 2019. Available: http://arxiv.org/abs/1912.0776844. Borji A. Pros and Cons of GAN Evaluation Measures. arXiv [cs.CV]. 2018. Available: http://arxiv.org/abs/1802.0344645. Salimans T, Goodfellow I, Zaremba W, Cheung V, Radford A, Chen X, et al. Improved Techniques for Training GANs. In: Lee DD, Sugiyama M, Luxburg UV, Guyon I, Garnett R, editors. Advances in Neural Information Processing Systems 29. Curran Associates,Inc.; 2016. pp. 2234–2242.46. Heusel M, Ramsauer H, Unterthiner T, Nessler B, Hochreiter S. GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium. arXiv [cs.LG]. 2017. Available: http://arxiv.org/abs/1706.085006847. Frid-Adar M, Klang E, Amitai M, Goldberger J, Greenspan H. Synthetic Data Augmentation using GAN for Improved Liver Lesion Classification. arXiv [cs.CV]. 2018. Available: http://arxiv.org/abs/1801.0238548. Shrivastava A, Pfister T, Tuzel O, Susskind J, Wang W, Webb R. Learning from Simulated and Unsupervised Images through Adversarial Training. arXiv [cs.CV]. 2016. Available: http://arxiv.org/abs/1612.0782849. Antoniou A, Storkey A, Edwards H. Data Augmentation Generative Adversarial Networks. arXiv [stat.ML]. 2017. Available: http://arxiv.org/abs/1711.0434050. Motamed S, Khalvati F. Inception Augmentation Generative Adversarial Network. arXiv [cs.CV]. 2020. Available: http://arxiv.org/abs/2006.0362251. Lee H, Kim J, Kim EK, Kim S. Wasserstein Generative Adversarial Networks Based Data Augmentation for Radar Data Analysis. NATO Adv Sci Inst Ser E Appl Sci. 2020;10: 144952. Zhang K, Zhong G, Dong J, Wang S, Wang Y. Stock Market Prediction Based on Generative Adversarial Network. Procedia Comput Sci. 2019;147: 400–406.53. Koshiyama A, Firoozye N, Treleaven P. Generative Adversarial Networks for Financial Trading Strategies Fine-Tuning and Combination. arXiv [cs.LG]. 2019. Available: http://arxiv.org/abs/1901.0175154. Wiese M, Knobloch R, Korn R, Kretschmer P. Quant GANs: deep generation of financial time series. Quantitative Finance. 2020. pp. 1–22. doi:10.1080/14697688.2020.173042655. Yoon J, Jarrett D, van der Schaar M. Time-series Generative Adversarial Networks. In:Wallach H, Larochelle H, Beygelzimer A, d\textquotesingle Alché-Buc F, Fox E, Garnett R, editors. Advances in Neural Information Processing Systems 32. Curran Associates,Inc.; 2019. pp. 5508–5518.56. Esteban C, Hyland SL, Rätsch G. Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs. arXiv [stat.ML]. 2017. Available:http://arxiv.org/abs/1706.0263357. Hartmann KG, Schirrmeister RT, Ball T. EEG-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signals. arXiv [eess.SP]. 2018. Available: http://arxiv.org/abs/1806.0187558. Wiese M, Bai L, Wood B, Buehler H. Deep Hedging: Learning to Simulate Equity Option Markets. arXiv [q-fin.CP]. 2019. Available: http://arxiv.org/abs/1911.0170059. Takahashi S, Chen Y, Tanaka-Ishii K. Modeling financial time-series with generative adversarial networks. Physica A: Statistical Mechanics and its Applications. 2019;527:121261.60. Guo Z, Wan Y, Ye H. A data imputation method for multivariate time series based on generative adversarial network. Neurocomputing. 2019;360: 185–197.61. Marti G. CORRGAN: Sampling Realistic Financial Correlation Matrices Using Generative Adversarial Networks. ICASSP 2020 - 2020 IEEE International Conferenceon Acoustics, Speech and Signal Processing (ICASSP). 2020.69 doi:10.1109/icassp40776.2020.905327662. Li D, Chen D, Jin B, Shi L, Goh J, Ng S-K. MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks. Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time Series. 2019. pp. 703–716.doi:10.1007/978-3-030-30490-4_56ORIGINAL71783907.2021.pdf71783907.2021.pdfMaestría en Ingeniería - Analíticaapplication/pdf3165696https://repositorio.unal.edu.co/bitstream/unal/79374/4/71783907.2021.pdf9ae5e37d6e48c21e8f3b197eb4118569MD54LICENSElicense.txtlicense.txttext/plain; charset=utf-83964https://repositorio.unal.edu.co/bitstream/unal/79374/5/license.txtcccfe52f796b7c63423298c2d3365fc6MD55CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-81025https://repositorio.unal.edu.co/bitstream/unal/79374/6/license_rdf84a900c9dd4b2a10095a94649e1ce116MD56THUMBNAIL71783907.2021.pdf.jpg71783907.2021.pdf.jpgGenerated Thumbnailimage/jpeg5028https://repositorio.unal.edu.co/bitstream/unal/79374/7/71783907.2021.pdf.jpg6383cd9c985fd051199afddf80fe6aa1MD57unal/79374oai:repositorio.unal.edu.co:unal/793742023-10-24 09:53:04.233Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.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

Generación de series de tiempo financieras sintéticas para "data augmentation" usando redes neuronales generativas adversarias (GAN)

Publicaciones similares