A Practical Approach to Timeseries Forecasting Using Python - Quiz Solution - Machine Learning in Time Series Forecasti

The video tutorial covers the use of ARIMA and related models 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 concept of 'order' and the role of exogenous variables in the Cerimax model. Finally, it addresses the seasonal moving average order in Cerima, represented as 'Q'. The next module will explore newer forecasting methodologies.

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

• ARIMA

• Decision Tree

• K-Means Clustering

• Linear Regression

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

• Cost and Revenue

• Time and Attribute

• Price and Demand

• Supply and Demand

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

• Polyvariate

• Multivariate

• Univariate

• Bivariate

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

• Level

• Rank

• Degree

• Order

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

• Endogenous Variables

• Exogenous Variables

• Independent Variables

• Dependent Variables