Forecasting Supermarket Sales Using Exponentially Weighted Quantile Regression
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...
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| Format: | Journal article |
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2007
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| _version_ | 1797105600875724800 |
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| author | Taylor, J |
| author_facet | Taylor, J |
| author_sort | Taylor, J |
| collection | OXFORD |
| description | 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. |
| first_indexed | 2024-03-07T06:49:41Z |
| format | Journal article |
| id | oxford-uuid:fc1ce78b-2155-408c-b8d6-2c4389997e76 |
| institution | University of Oxford |
| last_indexed | 2024-03-07T06:49:41Z |
| publishDate | 2007 |
| record_format | dspace |
| spelling | oxford-uuid:fc1ce78b-2155-408c-b8d6-2c4389997e762022-03-27T13:18:28ZForecasting Supermarket Sales Using Exponentially Weighted Quantile RegressionJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:fc1ce78b-2155-408c-b8d6-2c4389997e76Saïd Business School - Eureka2007Taylor, JInventory 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. |
| spellingShingle | Taylor, J Forecasting Supermarket Sales Using Exponentially Weighted Quantile Regression |
| title | Forecasting Supermarket Sales Using Exponentially Weighted Quantile Regression |
| title_full | Forecasting Supermarket Sales Using Exponentially Weighted Quantile Regression |
| title_fullStr | Forecasting Supermarket Sales Using Exponentially Weighted Quantile Regression |
| title_full_unstemmed | Forecasting Supermarket Sales Using Exponentially Weighted Quantile Regression |
| title_short | Forecasting Supermarket Sales Using Exponentially Weighted Quantile Regression |
| title_sort | forecasting supermarket sales using exponentially weighted quantile regression |
| work_keys_str_mv | AT taylorj forecastingsupermarketsalesusingexponentiallyweightedquantileregression |