Parameter estimation in double exponential smoothing using genetic algorithm / Foo Fong Yeng, Lau Gee Choon and Zuhaimy Ismail

In last decade, there has been increasing interest in simulating the natural evolutionary process in solving hard optimization problems. Genetic Algorithm (GA) is numerical optimization algorithm inspired by both natural selection and natural genetics. The method is general and capable of being applied to an extremely wide range of problems. Exponential smoothing is a simple extrapolative method that seeks to identify pattern of past data. Double Exponential Smoothing is one of the smoothing method which handle time series data with trend. The determination of parameter in Double Exponental Smoothing is difficult and crucial. Trial and error often serves as the best method to determine the parameter. Therefore, a good optimization technique is required for identify the best parameter in minimizing the forecast errors. The objective of this research is to estimate the Double Exponential Smoothing by using Genetic Algorithm Mechanism. The expected result of this research is Genetic Algorithm to able search for the best parameter in Double Exponential Smoothing.