Demand Forecasting based on Pairwise Item Associations
| Autor: | Ayhan Demiriz |
|---|---|
| Rok vydání: | 2014 |
| Předmět: |
Negative Association
Combined Forecast Computer science Constrained Clustering Correlation and dependence Regression analysis 02 engineering and technology Demand forecasting Overfitting Retail Demand Forecasting computer.software_genre 020204 information systems Linear regression 0202 electrical engineering electronic engineering information engineering Econometrics General Earth and Planetary Sciences 020201 artificial intelligence & image processing Pairwise comparison Data mining Association Mining Transaction data computer k-means Clustering General Environmental Science |
| Zdroj: | Complex Adaptive Systems |
| ISSN: | 1877-0509 |
| DOI: | 10.1016/j.procs.2014.09.091 |
| Popis: | Forecasting is an essential task conducted regularly by competitive retailers around the world. Most retail decisions are made based on the demand forecasts which may or may not be accurate in the first place. In this study, a framework for forecasting weekly demands of retail items is proposed via linear regression models within item groups that incorporate both positive and negative item associations. In addition to pairwise item associations found by utilizing transactional data, our framework incorporates item similarities based on weekly sales figures to group the similar items. Grouping items can be regarded as a form of variable selection to prevent the overfitting in the prediction models. The regression results of the framework and benchmark linear regression models are reported for a real world dataset provided by an apparel retailer. The results show that the regression models provide better estimates within multi-item groups compared to the single item models. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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