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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.

Persistent left superior vena cava (PLSVC) can be incidentally detected during pacemaker implantation from the left pectoral side. Optimal site pacing is technically difficult, and lead stability of the right ventricle (RV) can lead to such a situation. We describe a case of successful single-chamber pacemaker implantation in a 76-year-old woman with a PLSVC and concomitant agenesis of the right-sided superior vena cava, after failed attempts with the conventional procedure. The pacemaker had been working well after 12 months of follow-up.

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