Data Science - Time Series Forecasting with Facebook Prophet in Python - Prophet Section Summary

What is a key difference between Prophet and autoregressive models?

• Prophet is not suitable for business use cases.

• Prophet uses time as its only regressor.

• Prophet requires a large amount of data.

• Prophet cannot handle missing data.

How does Prophet handle holidays in its model?

• It requires external data for holidays.

• It models holidays as random events.

• It uses built-in functions or allows manual entry.

• It ignores holidays completely.

What is the first step in using Prophet for forecasting?

• Creating a placeholder data frame.

• Fitting the model with data.

• Performing cross-validation.

• Instantiating a Prophet object.

What must be included in the data frame when fitting a Prophet model?

• A column for every possible regressor.

• Columns DS and Y.

• Only the target variable.

• A column for each seasonality type.

What is a benefit of using cross-validation with Prophet?

• It eliminates the need for a data frame.

• It simplifies the model fitting process.

• It allows for real-time predictions.

• It helps compute performance metrics.