The rapid growth of the Internet has led to a vast increase in website traffic data. Accurately forecasting website  traffic is essential for informed decision-making and future planning. This study comprehensively analyzed four methods for forecasting website traffic time series data: Autoregressive Integrated Moving Average (Hereafter ARIMA), Prophet, Long Short-Term Memory (Hereafter LSTM), and Hybrid Long Short-Term Memory - Gated Recurrent Unit Recurrent Neural Network (Hereafter LSTM-GRU RNN). The study used Wikipedia Pageviews Dataset using API and Google Analytics data to train and test the forecasting models. The empirical analysis evaluated the accuracy and ability of each method to capture trends and seasonality. The results showed that the LSTM-GRU model with 50 epochs has the lowest MSE value of 0.0057022 and the lowest RMSE value of 0.075513. The LSTM model with 100 epochs has a low MSE value of 0.0057916 and a low RMSE value of 0.0761028, comparable to the LSTM-GRU model with 50 epochs.