Authors:
Rejwan Bin Sulaiman, G. Hariprasath, P. Dhinakaran, Utku Kose

Addresses:
1Department of Computer Science and Technology, Arden University, Middlemarch Park, United Kingdom. 2,3Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram,  Chennai, Tamil Nadu, India. 4Department of Computer Engineering, Suleyman Demirel University, Karasai, Turkey. rbsulaiman@arden.ac.uk1, hg8694@srmist.edu.in2, pp4417@srmist.edu.in3, utkukose@sdu.edu.tr4, 

Abstract:

Forecasting web traffic is critical for website owners, marketers, and organizations to make educated decisions, plan for future development, properly manage resources, and optimize their online presence. Handling online traffic was a simpler and less complicated procedure in the early days of the internet and web development compared to today's standards. The internet was still in its early stages, and websites were simpler. In recent years, handling online traffic with machine learning (ML) time series models have gotten more complex. Machine learning algorithms may give accurate projections and useful insights into online traffic trends. Using Facebook Prophet, a popular forecasting toolkit, this model explains the time series forecasting process and performance evaluation for online traffic data. Prophet's ability to handle complicated time series data with many seasonal components and holidays has won its appeal. Moving Average (MA) models were used for forecasting in time, but there are certain limits and drawbacks to using time series data to capture and forecast underlying trends. MA is specifically designed for short-term forecasting, capturing short-term dependencies and random fluctuations. However, the Prophet model is designed to handle time series data with various seasonal patterns, such as daily, weekly, and annual seasonality. We provide a detailed Explanation of the Prophet Time series model and Evaluation for web traffic data using Facebook Prophet, focusing on understanding model performance and visualization.

Keywords: Moving Average; Prophet of Forecasting; Time Series; Web Traffic; Seasonality of Facebook; Online Traffic Trends; forecasting toolkit; Handling Online Traffic.

Received on: 12/02/2023, Revised on: 27/04/2023, Accepted on: 01/08/2023, Published on: 29/11/2023

FMDB Transactions on Sustainable Computer Letters, 2023 Vol. 1 No. 3, Pages: 161-177

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