Abstract
This project makes use of the ARIMA (AutoRegressive integrated moving common) model to forecast web site visitors on weather-related web sites, analyzing how temperature fluctuations affect tourist numbers. ancient web visitors and temperature records are amassed, preprocessed, and analyzed. The ARIMA version is enhanced by way of incorporating temperature as an external regressor, optimizing forecasting accuracy via cautious parameter tuning. This method is evaluated towards traditional models to assess its effectiveness. The findings reveal that integrating temperature records notably improves predictive overall performance, supplying precious insights for managing web content based totally on environmental elements and predicting visitors developments with more precision.
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