Name
Title | ||
|---|---|---|
Demand-Prediction Architecture For Distribution Businesses Based On Multiple Rnns With Alternative Weight Update |
Abstract | ||
|---|---|---|
Predicting future demand is important for reducing costs, such as under- and over-stocking cost, in distribution business. To predict item demand, a prediction model such as an autoregressive model, directly uses order histories. It is difficult to manage models since the number of models equals that of items with in such an approach. It is not easy to apply of multi-step prediction. In this research, we propose an asynchronous-updating heterogeneous stacking model (AHSM) which is based on recurrent neural networks (RNNs). AHSM has three modules for prediction: feature extractor, predictor, and inner-state generator. By using the inner-state generator, the model enables stable learning and accurate prediction. We applied AHSM to demand prediction and compared it with other models, i.e., the auto-regressive, integral and moving average model and Prophet, and RNN-based model. The results indicate that AHSM enables the accurate demand prediction even in multi-step prediction. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-30490-4_39 | ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2019: TEXT AND TIME SERIES, PT IV |
Keywords | DocType | Volume |
Recurrent neural network, Deep learning, Demand prediction, Industrial use | Conference | 11730 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuya Okadome | 1 | 0 | 0.34 |
| Wenpeng Wei | 2 | 1 | 1.03 |
| Ryo Sakai | 3 | 0 | 0.34 |
| Toshiko AIZONO | 4 | 36 | 3.14 |