• Akanksha Prakash Hodage
• Mayuri Yashwant Patil
• Tanaya Shashikant Chabukswar
• Prof. S.R. Desai

By offering insights into potential website visitor patterns, web traffic time series forecasting is vital to many different organisations and industries. Businesses can improve customer experience overall, resource allocation, marketing tactics, inventory control, and pricing by using accurate forecasts. The notion of web traffic time series forecasting is investigated in this abstract, emphasising its importance and prospective applications. It includes forecasting methods like auto-ARIMA and exponential smoothing, which are frequently used to identify trends and generate forecasts based on past online traffic data. The abstract also discusses the significance of including outside data sources, sophisticated machine learning methods, and real-time forecasting capabilities for increased accuracy. The ultimate objective is to equip companies with the tools they need to use web traffic time series forecasting to improve customer happiness, make smart decisions, and increase operational efficiency

web traffic

"WEB TRAFFIC TIME SERIES FORECASTING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 5, page no.o33-o37, May-2023, Available :http://www.jetir.org/papers/JETIR2305F04.pdf

"WEB TRAFFIC TIME SERIES FORECASTING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 5, page no. ppo33-o37, May-2023, Available at : http://www.jetir.org/papers/JETIR2305F04.pdf