Demand Forecasting using Long Short-Term Memory Neural Networks

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ID: 282987
2020
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Abstract
In this paper we investigate to what extent long short-term memory neural networks (LSTMs) are suitable for demand forecasting in the e-grocery retail sector. For this purpose, univariate as well as multivariate LSTM-based models were developed and tested for 100 fast-moving consumer goods in the context of a master's thesis. On average, the developed models showed better results for food products than the comparative models from both statistical and machine learning families. Solely in the area of beverages random forest and linear regression achieved slightly better results. This outcome suggests that LSTMs can be used for demand forecasting at product level. The performance of the models presented here goes beyond the current state of research, as can be seen from the evaluations based on a data set that unfortunately has not been publicly available to date.
Reference Key
neumann2020demand Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Marta Gołąbek; Robin Senge; Rainer Neumann
Journal arXiv
Year 2020
DOI
DOI not found
URL
http://arxiv.org/abs/2008.08522v1
Keywords
cs.lg stat.ml i.2.6

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