A Practical Approach to Timeseries Forecasting Using Python - Autoregression

The video tutorial introduces the concept of auto regression, explaining how it uses past data to predict future values. It details the role of the parameter P in determining time lags and provides an example using a milk distribution company. The tutorial also covers the implementation of auto regression in Python, highlighting necessary modules like TQDM and Evaluate.

What does the term 'order' in auto regression refer to?

• The number of future values predicted

• The number of preceding inputs used

• The number of variables in the model

• The number of coefficients in the model

How does the parameter 'P' affect the auto regression model?

• It determines the number of future predictions

• It sets the number of past values considered

• It specifies the number of coefficients used

• It defines the number of variables in the model

If 'P' is set to 3, which past values are used for prediction?

• t-2, t-3, t-4

• t-1, t-3, t-4

• t-1, t-2, t-3

• t-1, t-2, t-4

In the milk distribution example, what is calculated to determine significant lags?

• PACF values

• Time series averages

• ACF values

• Regression coefficients

What is the significance of a PACF value being above a threshold?

• It shows a model error

• It signifies a significant lag for analysis

• It represents a constant value

• It indicates a future prediction

Which module is used to implement auto regression in Python?

• matplotlib

• statsmodels

• pandas

• numpy

What is the purpose of the TQDM module in the Python implementation?

• To show progress of the algorithm

• To import data

• To evaluate algorithms

• To visualize data