期刊
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
卷 58, 期 16, 页码 4964-4979出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/00207543.2020.1735666
关键词
deep learning; LSTM networks; random forests; multi-channel; retail
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.
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