Abstract—In this paper, we provide a robust forecasting model to predict phone prices in European markets using Long Short-Term Memory (LSTM) neural network and Support Vector Regression (SVR). We propose a comparison study of time series forecasting models for these two techniques. LSTM, due to its architecture, is considered as the perfect solution to problems not resolvable by classic Recurrent Neural Networks (RNNs). On the other hand, Support Vector Machines (SVMs) are a very powerful machine learning method for both classification and regression. After studying and comparing several univariate models, SVR and LSTM neural networks appear to be the most accurate ones. In addition, we compared multivariate models for both these techniques. Considering the multivariate approach, by introducing more variables, we obtain better prediction performance. In fact, the SVR model is able to predict the next day price with an root mean squared error (RMSE) of 33.43 euros with the univariate model. However, using multivariate models, LSTM RNN gives the most accurate prediction for the next day’s price with an RMSE of 23.640 euros.
Index Terms—Time series forecasting, LSTM neural network, support vector regression, e-commerce data, machine learning, deep learning.
Houda Bakir is with the Laboratory CEREP, Centre de Recherche en Productique Ecole Nationale Tunisia. Avenue Taha Hussein Montfleury, 1008 Tunis, Tunisia and now she is with Datavora Research and Development unit. 45 Avenue du Japon Tunis 1073, Tunisia (e-mail: email@example.com).
Ghassen Chniti was with the National Engineering School of Tunis, Tunisia 45 Avenue du Japon Tunis 1073, Tunisia (e-mail: ghasen.chniti@ datavora.com).
Hédi Zaher was with Laboratoire Tech-CICO (U.T.T), France. Now he is with Datavora. 45 Avenue du Japon Tunis 1073, Tunisia (e-mail: firstname.lastname@example.org).
Cite: Houda Bakir, Ghassen Chniti, and Hédi Zaher, "E-Commerce Price Forecasting Using LSTM Neural Networks," International Journal of Machine Learning and Computing vol. 8, no. 2, pp. 169-174, 2018.