Comparative analysis of short-term demand predicting models using ARIMA and deep learning

Halima Bousqaoui, Ilham Slimani, Said Achchab


The forecasting consists of taking historical data as inputs then using them to predict future observations, thus determining future trends. Demand prediction is a crucial component in the supply chain’s process that allows each member to enhance its performance and its profit. Nevertheless, because of demand uncertainty supply chains usually suffer from many problems such as the bullwhip effect. As a solution to those logistics issues, this paper presents a comparative analysis of four time series demand forecasting models; namely, the Autoregressive Integrated Moving Average (ARIMA) a statistical model, the multi-layer perceptron (MLP) a feedforward neural network, the Long Short-Term Memory model (LSTM) a recurrent neural network and the convolutional neural network (CNN or ConvNet) a deep learning model. The experimentations are carried out using a real-life dataset provided by a supermarket in Morocco. The results clearly show that the convolutional neural network gives slightly better forecasting results than the Long short-term memory network.


ARIMA; artificial neural network; convolutional neural network; LSTM; machine learning; supply chain management;

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International Journal of Electrical and Computer Engineering (IJECE)
p-ISSN 2088-8708, e-ISSN 2722-2578