Forecasting accuracy depends strongly on the data characteristic. Determining the data characteristic can help in developing an optimized forecasting strategy and selecting a suitable model [27]. Two indices were computed to identify the demand pattern of the data: average inter-demand interval (ADI) and square of the coefficient of variation (CV2). ADI is a measure of the demand regularity in time based on the average interval between demands while CV2 is an index that measures the variation in quantities. The two indices were used to classify LR data according to four different categories: smooth, intermittent, erratic, and lumpy [28].

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