In recent years, more emphasis on how to predict traffic of web pages has increased significantly and prompted the need for exploring various methods on how to effectively forecast future values of multiple times series. In this paper, we apply a forecasting model for the purpose of predicting web traffic. In particular, we use existing Web Traffic Time Series Forecasting dataset by Google to predict future traffic of Wikipedia articles. Predicting web traffic can help web site owners in many ways including: (a) determining an effective strategy for load balancing of web pages residing in the cloud, (b) forecasting future trends based on historical data and (c) understanding the user behavior. To achieve the goals of this research work, we built a time-series model that utilizes RNN seq2seq model. We then investigate the use of symmetric mean absolute percentage error (SMAPE) for measuring the overall performance and accuracy of the developed model. Finally, we compare the outcome of our developed model to existing ones to determine the effectiveness of our proposed method in predicting future traffic of Wikipedia articles.