TY - JOUR

T1 - Evaluating Predictive Count Data Distributions in Retail Sales Forecasting

AU - Kolassa, Stephan

PY - 2016/7/1

Y1 - 2016/7/1

N2 - Massive increases in computing power and new database architectures allow data to be stored and processed at finer and finer granularities, yielding count data time series with lower and lower counts. These series can no longer be dealt with using the approximative methods that are appropriate for continuous probability distributions. In addition, it is not sufficient to calculate point forecasts alone: we need to forecast entire (discrete) predictive distributions, particularly for supply chain forecasting and inventory control, but also for other planning processes. However, tools that are suitable for evaluating the quality of discrete predictive distributions are not commonly used in sales forecasting. We explore classical point forecast accuracy measures, explain why measures such as MAD, MASE and wMAPE are inherently unsuitable for count data, and use the randomized Probability Integral Transform (PIT) and proper scoring rules to compare the performances of multiple causal and noncausal forecasting models on two datasets of daily retail sales.

AB - Massive increases in computing power and new database architectures allow data to be stored and processed at finer and finer granularities, yielding count data time series with lower and lower counts. These series can no longer be dealt with using the approximative methods that are appropriate for continuous probability distributions. In addition, it is not sufficient to calculate point forecasts alone: we need to forecast entire (discrete) predictive distributions, particularly for supply chain forecasting and inventory control, but also for other planning processes. However, tools that are suitable for evaluating the quality of discrete predictive distributions are not commonly used in sales forecasting. We explore classical point forecast accuracy measures, explain why measures such as MAD, MASE and wMAPE are inherently unsuitable for count data, and use the randomized Probability Integral Transform (PIT) and proper scoring rules to compare the performances of multiple causal and noncausal forecasting models on two datasets of daily retail sales.

KW - Demand forecasting

KW - Density forecasting

KW - Error measures

KW - Intermittent demand

KW - Proper scoring rules

U2 - 10.1016/j.ijforecast.2015.12.004

DO - 10.1016/j.ijforecast.2015.12.004

M3 - Journal article

VL - 32

SP - 788

EP - 803

JO - International Journal of Forecasting

JF - International Journal of Forecasting

SN - 0169-2070

IS - 3

ER -