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.

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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