| dc.contributor.advisor | Sánchez-Torres, Juan D. | |
| dc.contributor.author | Muñoz-Elguezábal, Juan F. | |
| dc.date.accessioned | 2021-07-16T21:56:56Z | |
| dc.date.accessioned | 2023-03-21T16:40:55Z | |
| dc.date.available | 2021-07-16T21:56:56Z | |
| dc.date.available | 2023-03-21T16:40:55Z | |
| dc.date.issued | 2021-05 | |
| dc.identifier.citation | Muñoz-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.uri | https://hdl.handle.net/20.500.12032/73635 | |
| dc.description | Financial 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.language.iso | eng | es_MX |
| dc.publisher | ITESO | es_MX |
| dc.rights.uri | http://quijote.biblio.iteso.mx/licencias/CC-BY-NC-ND-2.5-MX.pdf | es_MX |
| dc.subject | Financial Machine Learning | es_MX |
| dc.subject | Genetic Methods | es_MX |
| dc.subject | Continuous Futures | es_MX |
| dc.subject | Financial Time Series Forecasting | es_MX |
| dc.title | Genetic Methods for Machine Learning Models: The Case of Financial Time Series Forecasting | es_MX |
| dc.type | info:eu-repo/semantics/masterThesis | es_MX |
