2024-03-28T09:12:18Zhttps://rei.iteso.mx/oai/requestoai:rei.iteso.mx:11117/76032021-10-27T03:00:52Zcom_11117_662col_11117_663
T-fold sequential-validation technique for out-of-distribution generalization with financial time series data
Muñoz-Elguezábal, Juan F.
Sánchez-Torres, Juan D.
Financial Machine Learning
Cross-Validation
Time Series Forecasting
Learning Theory
The temporal structure in financial time series (FTS) data demands non-trivial considerations in the use of cross-validation (CV). Such frequently used technique is based on statistical learning theory, which is founded on the assumption that training samples are i.i.d. Although there is progress in studying fundamental phenomenons in certain learning methods such as feature selection imbalance during the learning stage, it is currently widely accepted that there will be no reason to expect good out of sample results from a learning process without such strong assumption. In FTS, there are conditions under which sub-sampling data leads to overshadow the effect of non-deterministic relationships between features and the target variable among different samples. Such effect remains unnoticed given the use of the additivity property in the decomposition of objective functions for the Learning Process. Moreover, it reduces to a particular operation the relationship among samples without information attribution. We present a technique that controls information leakage and decomposes the global probability distribution into local probability distributions, providing identification of each sample contribution to the learning process, maintaining information sparsity, therefore, relaxing the effects of the i.i.d. assumption. Parametric stability, as a result, is presented for exchange rate prediction using different predictive models.
2021-10-27T00:23:04Z
2021-10-27T00:23:04Z
2021-06
info:eu-repo/semantics/conferencePoster
Muñoz-Elguezábal, J. F. & Sánchez-Torres, J. D. (2021). T-fold sequential-validation technique for out-of-distribution generalization with financial time series data. 4th International Conference on Econometrics and Statistics.
https://hdl.handle.net/11117/7603
eng
http://quijote.biblio.iteso.mx/licencias/CC-BY-NC-2.5-MX.pdf
International Conference on Econometrics and Statistics
oai:rei.iteso.mx:11117/50892020-02-12T07:02:41Zcom_11117_662col_11117_663
Memorias del Séptimo Foro de la Enseñanza de las Matemáticas
Nuño-Sánchez, Saúl A.
Ortiz-Cadena, Elihu B.
Brun-Battistini, Dominique
Ruiz-Cruz, Riemann
Barraza-García, Zeidy M.
Eccius-Wellmann, Clara C. C.
Pantoja-Rangel, Rafael
Mondragón-Suárez, José H.
Sandoval-Villalbazo, Alfredo
Acosta-González, Adriana
Gómez-Salazar, David
González-Fernández, Belinka
Valdez-Gutiérrez, Margarita
Carrasco-Romo, Sergio
Gómez-García, Dario E.
Vargas-Salcedo, José G.
González-Bermúdez, Cristina
Ibarra-González, Karla P.
Enseñanza de las Matemáticas
Educación
Matemáticas
Educación Superior
Práctica Docente
Ingeniería
Conocimientos Matemáticos
En el Séptimo Foro de Enseñanza de las Matemática Ibero 2017 se abordaron temas relacionados con el uso de la historia de las matemáticas en el aula, las transformaciones en las habilidades, destrezas y conocimientos en los alumnos universitarios en la primera década del 2000, el uso de plataformas digitales en la enseñanza de las matemáticas; se expuso sobre situaciones problema en la vida cotidiana relacionados con las matemáticas escolares y la modelación matemática, sobre la historia de la enseñanza de las matermáticas, entre otros temas.
2017-11-09T20:43:48Z
2017-11-09T20:43:48Z
2017-09
info:eu-repo/semantics/conferencePaper
(2017) Memorias del Séptimo Foro de la Enseñanza de las Matemáticas Ibero 2017, sede ITESO. Guadalajara, Jalisco: ITESO.
http://hdl.handle.net/11117/5089
spa
Séptimo Foro de la Enseñanza de las Matemáticas Ibero;
http://quijote.biblio.iteso.mx/licencias/CC-BY-NC-2.5-MX.pdf
ITESO