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 simplifies 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.
In this study, the forecasting accuracy of a new forecasting method that is alternative to two major forecasting approaches: exponential smoothing (ES) and ARIMA, will be evaluated. Using the results from the M3-competition, the forecasting performance of this method will be compared to not only these two major approaches but also to other successful methods derived from these two approaches with respect to simplicity and cost in addition to accuracy.
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