Modelling and forecasting of web traffic using Holt's linear, bats and TBATS models

Abstract

In the recent era, internet consumption has increased. Due to this heavy and regular use of internet web traffic is increased. Sometimes due to high web traffic server may also affect. In this study, cumulative data on the number of visitors to Wikipedia, Facebook, Energy, Android, and Apple, are analyzed in detail. Some descriptive statistics of visitor’s pages in Wikipedia are given such as mean, minimum, maximum, standard deviation, skewness and kurtosis. The present study used different time series models like Holt’s Linear Trend, BATS and TBATS for different web pages. From the results, it was found that the Android page as well as apple page in Wikipedia holt’s Linear Trend model performance is better compared to other models. This kind of projection is helpful for web traffic to solve the server breakdown problem for larger users of the server. These Wikipedia pages have been chosen to Forecast the number of visitors to these pages through time series models BATS, TBATS, and Holt's Linear Trend Model in order to face future problems to mitigate over loading that may occur with the increase in the number of visitors to these pages and also to experiment the suitability of these models of Time series to Forecast the number of visitors and that to achieve the highest level of accuracy. MAPE for accuracy was used to compare model performance.