Forecasting daily supermarket sales using exponentially weighted quantile regression

Forecasting daily supermarket sales using exponentially weighted quantile regression

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Article ID: iaor20084230
Country: Netherlands
Volume: 178
Issue: 1
Start Page Number: 154
End Page Number: 167
Publication Date: Apr 2007
Journal: European Journal of Operational Research
Authors:
Keywords: time series & forecasting methods, retailing
Abstract:

Inventory control systems typically require the frequent updating of forecasts for many different products. In addition to point predictions, interval forecasts are needed to set appropriate levels of safety stock. The series considered in this paper are characterised by high volatility and skewness, which are both time-varying. These features motivate the consideration of forecasting methods that are robust with regard to distributional assumptions. The widespread use of exponential smoothing for point forecasting in inventory control motivates the development of the approach for interval forecasting. In this paper, we construct interval forecasts from quantile predictions generated using exponentially weighted quantile regression. The approach amounts to exponential smoothing of the cumulative distribution function, and can be viewed as an extension of generalised exponential smoothing to quantile forecasting. Empirical results are encouraging, with improvements over traditional methods being particularly apparent when the approach is used as the basis for robust point forecasting.

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