| Summary: | Evaluation of forecast accuracy is very much influenced by the choice of
accurate measurement since it can produce different conclusion from the empirical
results. Thus, it is important to use appropriate measurement in accordance to the
purpose of forecasting. Commonly, accuracy is measured in terms of error
magnitude. However, directional accuracy is as important as error magnitude
especially in economics since it considers directional movement of the data. This
research attempted to combine the two types of measurements by introducing a new
element, the slope value. This proposed measure is known as square error modified
of directional accuracy (SE-mDA). Before that, the existing directional change error
measurement was modified by comparing the direction of two subsequent forecasts
data with two subsequent observed data. Empirical application utilizing the monthly
data of Malaysia and Bali tourism demand was used to compare the forecast
performance between SARIMA, time series regression, Holt-Winter, intervention
neural network and fuzzy time series. The root mean square error, mean absolute
percentage error, mean absolute deviation, Fisher’s exact test, Chi-square test,
directional accuracy, directional value and the modified of directional change error
were used in forecast accuracy evaluation. The best forecast model in terms of
SE-mDA for the data of Malaysia and Bali are Holt-Winters and neural network,
respectively. The main conclusion from this study is that SE-mDA is able to
improve the forecasting performance assessment of error magnitude measurement by
considering the directional movements. At the same time it also enhances the
available directional accuracy measurement by taking into account the difference
between slopes of forecast data and observed data. These improvements will help
forecaster to choose the best forecasting method or model so as to produce the most
accurate forecast.
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