Modified Holt`s linear trend method

Exponential smoothing models are simple, accurate and robust forecasting  models and because of these they are widely applied in the literature. Holt`s linear trend method is a valuable extension of exponential  smoothing that helps deal with trending data. In this study we propose a modified version of Holt`s linear trend method that eliminates  the initialization issue faced when fitting the original model and simplies the optimization process. The proposed method is compared  empirically with the most popular forecasting algorithms based on exponential smoothing and Box-Jenkins ARIMA with respect to its predictive performance on the M3-Competition data set and is shown to outperform its competitors.