Genetic Methods for Machine Learning Models: The Case of Financial Time Series Forecasting

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dc.contributor.advisorSánchez-Torres, Juan D.
dc.contributor.authorMuñoz-Elguezábal, Juan F.
dc.date.accessioned2021-07-16T21:56:56Z
dc.date.available2021-07-16T21:56:56Z
dc.date.issued2021-05
dc.descriptionFinancial time series forecasting certainly is the case of a predictive modeling process with many challenges, mainly because the temporal structure of the data. Genetic programming, as a particular variation of genetic algorithms, can be used to as a feature engineering, importance and selection process all at once, it can provide highly interpretable symbolic features that have low colinearity among them and yet high correlation with a target variable. We present the use of such method for generating symbolic features from endogenous linear and autoregressive variables, along with a Multi-Layer Perceptron, to construct a binary predictor for the price of Continuous Future Contracts of the Usd/Mxn intra-day exchange rate. The proposition of this work is three fold, first is stated a variation to formulate the classical regression problem of forecasting a continuous value, into a classification problem of forecasting a discrete and binary value, also, in order to address the feature engineering step, the use of Genetic Programming is proposed for producing non linear variables highly correlated with a target and highly uncorrelated with each other, and finally, variations on the performance metrics and Folds of data to perform the training process are implemented. The results are presented for a Logistic regression and a Multi-Layer Perceptron applied to 6 years of historical prices for the UsdMxn Financial Future contract.es_MX
dc.identifier.citationMuñoz-Elguezábal, J. F. (2021). Genetic Methods for Machine Learning Models: The Case of Financial Time Series Forecasting. Trabajo de obtención de grado, Maestría en Ciencia de Datos. Tlaquepaque, Jalisco: ITESO.es_MX
dc.identifier.urihttps://hdl.handle.net/11117/7449
dc.language.isoenges_MX
dc.publisherITESOes_MX
dc.rights.urihttp://quijote.biblio.iteso.mx/licencias/CC-BY-NC-ND-2.5-MX.pdfes_MX
dc.subjectFinancial Machine Learninges_MX
dc.subjectGenetic Methodses_MX
dc.subjectContinuous Futureses_MX
dc.subjectFinancial Time Series Forecastinges_MX
dc.titleGenetic Methods for Machine Learning Models: The Case of Financial Time Series Forecastinges_MX
dc.typeinfo:eu-repo/semantics/masterThesises_MX
dc.type.versioninfo:eu-repo/semantics/acceptedVersiones_MX

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