Predicción de series de tiempo usando un modelo híbrido basado en la descomposición wavelet
Abstract (en)
El pronóstico de series de tiempo que exhiben una estructura de segundo orden que vara en función del tiempo ha recibido especial atención debido a la dificultad de obtener buenos pronósticos, especialmente cuando existe una estructura poco homogénea al final de los datos. En este trabajo, se usa una metodología adecuada para pronosticar series de tiempo, con un alto nivel de ruido que evidencien no estacionariedad. Especialmente, se combina la transformación wavelet discreta de máximo traslape (MODWT) con el modelo ARFIMA-HYGARCH y redes neuronales. Ambos modelos se aplican para pronosticar la tasa de cambio USD/COP. Los resultados sugieren que la metodología basada en wavelets y redes neuronales, proveen pronósticos más precisos para pronosticar una apreciación/depreciación del tipo de cambio.
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