Natural lenguaje processing para la predicción de series de tiempo en el mercado de commodities energético con el uso de modelos en inteligencia artificial

Vista sencilla de ítem
dc.contributor.authorAlba Suárez, Miguel Antoniospa
dc.contributor.authorPineda Ríos, Wilmer Daríospa
dc.contributor.cvlachttp://scienti.colciencias.gov.co:8081/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001325299spa
dc.contributor.cvlachttp://scienti.colciencias.gov.co:8081/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001454199spa
dc.contributor.googlescholarhttps://scholar.google.com/citations?user=eVxlDqUAAAAJ&hl=esspa
dc.contributor.googlescholarhttps://scholar.google.es/citations?user=5KmOl5oAAAAJ&hl=esspa
dc.contributor.orcidhttps://orcid.org/0000-0002-1481-2486spa
dc.contributor.orcidhttps://orcid.org/0000-0001-7774-951Xspa
dc.coverage.campusCRAI-USTA Bogotáspa
dc.date.accessioned2020-07-29T22:10:44Zspa
dc.date.available2020-07-29T22:10:44Zspa
dc.date.issued2020-08spa
dc.descriptionLos negocios internacionales en los mercados financieros históricamente han venido utilizando herramientas de tipo estadístico como fundamental para describir y predecir el comportamiento de los activos financieros. Ante a realidad planteada surge en el escenario de hoy en el campo del data science, el uso de la inteligencia artificial, la cual se caracteriza en la utilizaciòn de herramientas propias del Natural Language Processing (NLP) como es el caso del análisis de texto y de sentimientos del mercado, cuyo objetivo es el de identificar, si el uso de texto en el análisis de series de tiempo resulta significativo a nivel predictivo en este tipo datos secuenciales. El trabajo propuesto tienen como objetivo predecir el comportamiento del mercado de commodities energeticoetico mediante la utilización de inteligencia artificial.spa
dc.description.domainhttp://unidadinvestigacion.usta.edu.cospa
dc.format.mimetypeapplication/pdfspa
dc.identifier.citationAlba, M. A., & Pineda, W. D., (S.F.) Natural lenguaje processing para la predicción de series de tiempo en el mercado de commodities energético con el uso de modelos en inteligencia artificial Bogotá: Universidad Santo Tomásspa
dc.identifier.doihttps://doi.org/10.15332/dt.inv.2020.01355spa
dc.identifier.urihttp://hdl.handle.net/11634/28677
dc.relation.referencesA. A., & A. P. (2015). Mastering probabilistic Graphial Models using Python . Birmingham B3 2PB, UK.: PACKT: publishing . Alba Acosta, M. (2018). Predictive capacity of deep learning models in cryptocurrency market analysis.spa
dc.relation.referencesAmjad, M. (2016). Prediction, Trading Bitcoin and Online Time Series. NIPS 2016 Time Series Workshop, 55. Barcelona. Amunategui, M. (2017, 11 2). Data Exploration & Machine Learning, Hands-on. Retrieved from Convolutional Neural Networks And Unconventional Data - Predicting The Stock Market Using Images: http://amunategui.github.io/unconventional-convolutional-networks/index.htmlspa
dc.relation.referencesBontempi,G. (2013). Machine Learning Strategies for Time Series Prediction. (B. d.-C. 212, Ed.) Machine Learning Summer School (Hammamet, 2013).spa
dc.relation.referencesBrownlee, J. (2017). Long Short-Term Memory Networks With Python: Develop Sequence Prediction Models With Deep Learning. Machine Learning Mastery. Cross Validated. (2015). A list of cost functions used in neural networks, alongside applications. Retrieved from https://stats.stackexchange.com/questions/154879/a-list-of-cost-functions-used-in-neural-networks-alongside-applicationsspa
dc.relation.referencesDe la Vega J & Garcia M, J. (2004). Análisis para negociar en el mercado de futuro. Estrategia Financiera, 34-41. Elidan, G. (2010). Copula bayesian networks. (H. U. Department of Statistics, Ed.) Advances in Neural Information Processing Systems, 24.spa
dc.relation.referencesFumo, D. (2017, June 15). https://towardsdatascience.com/types-of-machine-learning-algorithms-you-should-know-953a08248861. Retrieved from Towards Data Science: https://towardsdatascience.com/types-of-machine-learning-algorithms-you-should-know-953a08248861spa
dc.relation.referencesG. I., B. Y., & C. A. (2016). Deep Learning. MIT Press. Ghahramani, Z. (1997). Learning Dynamic Bayesian Networks. Department of computer science, University of Toronto. Google. (2017). AI adventures. Retrieved from Youtube: https://www.youtube.com/channel/UCJS9pqu9BzkAMNTmzNMNhvgspa
dc.relation.referencesH. S., & S. J. (1997, november 15). Long Short-Term Memory. Neural Computation, Volume 9(Issue 8). Heaton, J. (2013, 07). Bayesian Networks for Predictive Modeling. Forecasting & Futurism, 6. Jaakkola, T. (2006, Fall). Course materials for 6.867 Machine Learning. Document Downloaded on [11 04 2018]. Massachusetts Institute of Technology.spa
dc.relation.referencesJacks D., O'Rourke, K & Williamson. (2009). Coomodity price volatility an word market integration since 1700.spa
dc.relation.referencesJimsparkle. (2017). Understanding bitcoin market trend and feature using unsupervised Machine-Learning. Retrieved from steemit: https://steemit.com/bitcoin/@jimsparkle/understanding-bitcoin-market-trend-and-feature-using-unsupervised-machine-learningspa
dc.relation.referencesL. F.-F., J. J., & Y. S. (2017). Lecture 10 | Recurrent Neural Networks. Recurrent Neural Networks (pp. lecture 10-22). Stanford University. LeCun, Y., B. L., B. Y., & H. P. (1998). Gradient-based learning applied to document recognition. Proc. of the IEEE. Lewis, N. (2016). DEEP TIME SERIES FORECASTING With PYTHON.spa
dc.relation.referencesLiu, A. T. (2004). A Bayesian Network Approach to Explaining Time Series with Changing Structure. Department of Information Systems and Computing. M. L., & J. L. (2001). Recurrent neural networks (design and applications). The Mawson Lakes, SA Australia: CRC press.spa
dc.relation.referencesMargaritis, D. (2003, may). Learning Bayesian Network Model Structure from Data. 126. Pittsburgh, PA 15213, United States: School of Computer Science, Carnegie Mellon University.spa
dc.relation.referencesMarr, B. (2016, December 6). What Is The Difference Between Artificial Intelligence And Machine Learning? Retrieved from Forbes: https://www.forbes.com/sites/bernardmarr/2016/12/06/what-is-the-difference-between-artificial-intelligence-and-machine-learning/#7c2d4aea2742spa
dc.relation.referencesMcNally, S. (2016, 08 22). Predicting the price of Bitcoin using Machine Learning. MSc Reseach Project. Mihajlovic, & Petkovic. (2001). Dynamic Bayesian Networks: A State of the Art. CTIT Technical Report Series, 01-34.spa
dc.relation.referencesN. G., M. M., & N. A. (2011). Characterization of Dynamic Bayesian Network The Dynamic Bayesian Network as temporal network. (IJACSA) International Journal of Advanced Computer Science and Applications,, 8. Nielsen, M. (2017).spa
dc.relation.referencesNeural Networks and Deep Learning. Novick, P. (2011). Udacity.com. Retrieved from Intro to Artificial Intelligence: https://classroom.udacity.com/courses/cs271 Osterrieder, J. a. (2016, October 1). Bitcoin and Cryptocurrencies - Not for the Faint-Hearted. Advanced Risk & Portfolio Management Paper.spa
dc.relation.referencesPerasso, V. (2016, 10 02). Qué es la cuarta revolución industrial (y por qué debería preocuparnos). BBC NEWS . saini, G. (2017). artificial neuron consisting of dendrites,axon and threshold function. Artificial neural network.pngspa
dc.relation.referencesOwn work. SAS. (2018). Machine Learning: What it is and why it matters. Retrieved from SAS: https://www.sas.com/es_mx/insights/analytics/machine-learning.htmlspa
dc.relation.referencesScutari, M. (2015, 06). Bayesian Network Constraint-Based Structure Learning Algorithms: Parallel and Optimised Implementations in the bnlearn R Package. Journal of Statistical Software, VV, Issue II.spa
dc.relation.referencesTing Yu, N. V. (2013). Computational Intelligent Data Analysis for Sustainable Development. (V. Kumar, Ed.) University of Minnesota: CRC Press. UFLDL Tutorial . (2017, 05 12). Unsupervised Feature Learning and Deep Learning tutorial. Retrieved from UFLDL stanford: http://ufldl.stanford.edu/tutorial/supervised/OptimizationStochasticGradientDescent/spa
dc.relation.referencesWang, H. (2016, 04 07). Towards Bayesian Deep Learning: A Survey. (Stat.ML, Ed.) Zhou, Y. (2015, 08 08). New Techniques for Learning Parameters in Bayesian Networks. 164. Department of Computer Science.spa
dc.rightsAtribución-NoComercial-SinDerivadas 2.5 Colombia*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/2.5/co/*
dc.subject.keywordDeep learningspa
dc.subject.keywordArtificial intelligencespa
dc.subject.keywordNatural Language Processingspa
dc.subject.keywordArtificial neural networksspa
dc.subject.keywordFinancespa
dc.subject.keywordFinancial marketspa
dc.subject.keywordFinance systemspa
dc.subject.lembFinanzasspa
dc.subject.lembMercado financierospa
dc.subject.lembSistema financierospa
dc.subject.proposalInteligencia artificialspa
dc.subject.proposalNatural Language Processingspa
dc.subject.proposalRedes neuronales artificialesspa
dc.subject.proposalDeep learningspa
dc.titleNatural lenguaje processing para la predicción de series de tiempo en el mercado de commodities energético con el uso de modelos en inteligencia artificialspa
dc.type.categoryApropiación Social y Circulación del Conocimiento: Informes finales de investigaciónspa

Archivos

Bloque original

Mostrando 1 - 1 de 1
Miniatura
Nombre:
Miguel Antonio Alba Suárez - 1969001.pdf
Tamaño:
302.62 KB
Formato:
Adobe Portable Document Format
Descripción:
Descargar

Bloque de licencias

Mostrando 1 - 1 de 1
Thumbnail USTA
Nombre:
license.txt
Tamaño:
807 B
Formato:
Item-specific license agreed upon to submission
Descripción:
Descargar

Colecciones

Shibumi