Adaptive Neuro-Fuzzy Inference Systems with Heteroscedastic Errors for
This paper proposes a new kind of non-linear hybrid model. In the proposed model, mean non-linearity is represented by using an adaptive neuro-fuzzy inference system (ANFIS) whereas variance is represented using a conditional self-regressive heteroscedastic component. The mathematical formula for...
| Autor Principal: | Zapata Gómez, Elizabeth Catalina |
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| Otros Autores: | Velásquez Henao, Juan David, Smith Quintero, Ricardo Agustín |
| Formato: | info:eu-repo/semantics/article |
| Idioma: | spa |
| Publicado: |
Pontificia Universidad Javeriana
2008
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| Materias: | |
| Acceso en línea: |
http://revistas.javeriana.edu.co/index.php/cuadernos_admon/article/view/3909 |
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| Sumario: |
This paper proposes a new kind of non-linear hybrid model. In the proposed model, mean non-linearity is represented by using an adaptive neuro-fuzzy inference system (ANFIS) whereas variance is represented using a conditional self-regressive heteroscedastic component. The mathematical formula for this type of model is shown and a method to estimate it is proposed. In addition, a specification strategy is developed for the proposed model, based on a battery of statistical soft transaction regression (STR) tests and on verosimility radius testing. As a case study, the IBM stock closing price series dynamics were modeled, which is commonly used as a benchmark in the literature on time series. Results indicate that the model developed represents the dynamics of the studied series better than other models with similar characteristics. |
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