Abstract : Web traffic forecasting is a key topic since it has the potential to cause major problems with website functionality. Making predictions about future time series values is one of the most challenging problems, hence it has become a popular issue for research. As a result of the increased web traffic, the site may crash or load very slowly. Such disruptions may cause numerous disruptions for users, resulting in a lower user rating of the site and user migration to another site, which has an impact on the business. To predict online traffic, we created a forecasting model. The ARIMA model is used to forecast Web traffic time series. We used some of the information, such as the name of the page, the date it was seen, and the number of visits, to make more accurate predictions.
Keywords Web traffic prediction, ARIMA model, Time series forecasting, Data Collection and Feature Understanding.
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DOI:
10.17148/IJARCCE.2022.115100
