2020/03/16 by Sushil Punia, Κωνσταντίνος Νικολόπουλος, Konstantinos Nikolopoulos +4 · 197 citations
Decision Sciences · Engineering · Mathematics · #Artificial intelligence #Artificial neural network #Benchmark (surveying) #Computer science #Deep learning #Demand forecasting #Econometrics #Economics #Energy Load and Power Forecasting #Engineering #Forecasting Techniques and Applications #Machine learning #Mathematics #Multivariate statistics #Operations research #Random forest #Regression #Robustness (evolution) #Statistics #Stock Market Forecasting Methods #Term (time) #Variance (accounting)
paper · open access · doi:10.1080/00207543.2020.1735666
published in International Journal of Production Research 58(16), 4964-4979 (Taylor & Francis)
openalex publication_date 2020/03/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.