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.

Which of the following models is used to predict future data in a time series?

• ARIMA

• Linear Regression

• Decision Tree

• K-Means Clustering

In a forecasting problem, what are the two main variables involved?

• Time and Attribute

• Time and Location

• Attribute and Cost

• Time and Cost

What is the term used for forecasting with only one attribute?

• Trivariate

• Bivariate

• Univariate

• Multivariate

In auto regression, what is the term for the number of preceding inputs used to predict the next value?

• P

• R

• D

• Q

In the Cerimax model, what does the 'X' represent?

• Extra Variables

• Exogenous Variables

• External Variables

• Endogenous Variables