Open AccessComparative analysis of short-term demand predicting models using ARIMA and deep learning
Author(s) -
Halima Bousqaoui,
Ilham Slimani,
Saïd Achchab
Publication year - 2021
Publication title -
international journal of power electronics and drive systems/international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
eISSN - 2722-2578
pISSN - 2722-256X
DOI - 10.11591/ijece.v11i4.pp3319-3328
Subject(s) - autoregressive integrated moving average , computer science , demand forecasting , artificial intelligence , artificial neural network , autoregressive model , convolutional neural network , time series , perceptron , deep learning , term (time) , machine learning , recurrent neural network , data mining , econometrics , operations research , economics , engineering , physics , quantum mechanics
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.