Directional change error evaluation in time series forecasting

Error magnitude measurements are commonly used to assess various forecasting models or methods. However, accuracy in terms of error magnitude alone is not enough especially in the field of economics. The information on the directional behavior of the data is very important since if the forecast fails to predict the directional change effectively, it could cause huge negative impact on economic activities. Thus, in assessing economic forecast value, it is important to consider both the magnitudes and directional movements. The existing directional change error (DCE) was modified by comparing directional of two consecutive forecasts data with two consecutive actual data. The modified directional change error (mDCE) compares the directional between the actual and the forecast as a whole, however DCE compares them one by one. This gives mDCE an advantage as it provides overview information on the entire directional pattern of the data. Thus, an evaluation by mDCE would makes a directional forecast more reliable and forecaster could obtain better information on the depiction of the directional pattern of the data.