Deep learning with long short-term memory networks and random forests for demand forecasting in multi-channel retail
| Autor: | Konstantia Litsiou, Konstantinos Nikolopoulos, Sushil Punia, Surya Prakash Singh, Jitendra Madaan |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
021103 operations research Relation (database) business.industry Computer science Strategy and Management Deep learning 0211 other engineering and technologies 02 engineering and technology Management Science and Operations Research Demand forecasting Machine learning computer.software_genre Industrial and Manufacturing Engineering Random forest Long short term memory 020901 industrial engineering & automation Artificial intelligence business computer Multi channel |
| Popis: | This paper proposes a novel forecasting method that combines the deep learning method - long short-term memory (LSTM) networks and random forest (RF). The proposed method can model complex relationships of both temporal and regression type which gives it an edge in accuracy over other forecasting methods. We evaluated the new method on a real-world multivariate dataset from a multi-channel retailer. We benchmark the forecasting performance of the new proposition against neural networks, multiple regression, ARIMAX, LSTM networks, and RF. We employed forecasting performance metrics to measure bias, accuracy, and variance, and the empirical evidence suggests that the new proposition is (statistically) significantly better. Furthermore, our method ranks the explanatory variables in terms of their relative importance. The empirical evaluations are replicated for longer forecasting horizons, and online and offline channels and the same conclusions hold; thus, advocating for the robustness of our forecasting proposition as well as the suitability in multi-channel retail demand forecasting. |
| Databáze: | OpenAIRE |
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