Demand Prediction Architecture for Distribution Business by Adopting Multiple Recurrent Neural Networks

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  • 複数の再帰型ニューラルネットワークを用いた需要予測アーキテクチャの開発

Abstract

Due to change of market, an explosive variety of handled items brings considerable costs of both over-stocking and under-stocking to distributors. In this research, we propose the demand prediction architecture by adopting multiple recurrent neural network (RNN). The proposed model can handle various types of information, e.g., weather, store sales, by placing the RNNs at the input layer. We applied preposed model to a demand prediction problem using the open data which has the daily demands of 4,000 items. Results shows our proposed model achieves the accurate short- and long-term demand prediction

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