A Practical Approach to Timeseries Forecasting Using Python - SARIMA

The video tutorial introduces the SERIMA model, an extension of ARIMA that incorporates seasonal components. It explains the additional hyperparameters for seasonal autoregressive, differencing, and moving average components, as well as the period of seasonality. The tutorial covers both trend and seasonal parameters, detailing their roles in the model. It also compares SERIMA's performance to ARIMA and provides guidance on implementing SERIMA in Python, including generating graphs.

What is the main difference between ARIMA and SERIMA models?

• SERIMA adds seasonal components to ARIMA.

• SERIMA is used for non-time series data.

• SERIMA removes the need for differencing.

• SERIMA includes a multivariate time series component.

Which of the following is NOT a trend parameter in the SERIMA model?

• P

• D

• Q

• M

What does the 'M' parameter represent in the SERIMA model?

• The number of seasonal cycles

• The number of time steps for a single seasonal cycle

• The moving average order

• The differencing order

Which of the following is a seasonal parameter in the SERIMA model?

• Seasonal Autoregressive Order

• Moving Average Order

• Differencing Order

• Trend Order

How does the performance of SERIMA compare to ARIMA?

• SERIMA always performs better than ARIMA.

• SERIMA never performs better than ARIMA.

• SERIMA sometimes performs better than ARIMA.

• SERIMA is not comparable to ARIMA.