A Practical Approach to Timeseries Forecasting Using Python - Variations in SARIMA

The video tutorial explores the use of SARIMA models with different seasonal values, demonstrating how variations in seasonal order affect forecasting results. It highlights the impact of reducing seasonality, showing that when seasonality is minimized, the model behaves similarly to ARIMA. The tutorial applies these concepts to COVID data, emphasizing the importance of choosing the correct seasonal period. The project concludes with a summary of SARIMA variations and a preview of future projects.

What happens when the seasonal value is set to two months in the SARIMA model?

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Discuss the relationship between seasonality and the accuracy of forecasts in the context of COVID cases.

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