OPTIMIZACIÓN DE PARÁMETROS Y DE VALORES DE INICIO PARA EL MODELO DE HOLT BASADO EN SEÑALES DE RASTREO (PARAMETER AND INITIAL VALUES OPTIMIZATION FOR HOLT MODEL BASED ON TRACKING SIGNALS)
OPTIMIZACIÓN DE PARÁMETROS Y DE VALORES DE INICIO PARA EL MODELO DE HOLT BASADO EN SEÑALES DE RASTREO (PARAMETER AND INITIAL VALUES OPTIMIZATION FOR HOLT MODEL BASED ON TRACKING SIGNALS)
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Los modelos de series de tiempo son técnicas cuantitativas con frecuencia utilizadas para realizar pronósticos de variables, dentro de los cuales se encuentran los modelos de suavización, en particular el de suavización con ajuste de tendencia, llamado también modelo de Holt, que requiere la definición de los parámetros a y b y conocidos como coeficientes de suavización y de los valores de inicio que son fundamentales para su actualización. En este artículo se propone una forma de obtener estos valores mediante la optimización del rango de la señal de rastreo (TSR) que permitan lograr un modelo más confiable desde el punto de vista de la exactitud de los resultados y de su desempeño histórico. Se realizan algunas comparaciones con modelos propuestos que utilizan la desviación absoluta media (MAD) y el error cuadrado medio (MSE) las cuales son las medidas tradicionalmente utilizadas para determinar el grado de exactitud de un modelo, lográndose obtener un comportamiento mejor de modelo.
Abstract: Time series models are quantitative techniques commonly used to forecast the behavior of variables. These models include the exponential smoothing with trend or Holt model that requires the definition of the smoothing constants a and b and the initialization values, both required for the model upgrade. This paper proposes a different way to obtain the parameter values and initial conditions of the Holts model, optimizing the tracking signal range (TSR), in order to achieve a more robust model from the viewpoint of accuracy of the results and historical performance. Some comparisons between the proposed approach and the traditional methods based on the mean absolute deviation (MAD) and the mean square error (MSE) are provided. These are the measures traditionally used to determine the degree of accuracy of a model, and a better model performance is obtained.
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