Quiz Solution

The video tutorial covers the use of ARIMA and related models like RIMA, Cerimax, and SRIMA for forecasting future and past data in a series. It explains the two main variables in forecasting: time and attributes, and distinguishes between univariate and multivariate forecasting. The tutorial also discusses auto regression, highlighting the importance of the order (P) and the role of exogenous variables (X) in the Cerimax model. Additionally, it explains the seasonal moving average order in Cerima, represented as Q. The module concludes with a brief mention of upcoming content on newer forecasting methodologies.

In auto regression, what is the term used to describe the number of preceding inputs?

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How is the seasonal moving average order represented in the Cerima model?

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