Data Science - Time Series Forecasting with Facebook Prophet in Python - Prophet in Code: Holidays and Exogenous Regress

The lecture continues with building profit models for time series, focusing on incorporating holidays and exogenous regressors. It demonstrates creating a new profit model, M3, and adding country holidays for demonstration purposes. The lecture then explains adding exogenous regressors, including state and school holidays, and creating a new model, M4. The process involves dummy encoding categorical data and fitting the model with added regressors. The lecture concludes with forecasting and analyzing results, highlighting the importance of removing non-relevant days from the time series.

What is the focus of the lecture regarding profit models?

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How do holidays and exogenous regressors affect the profit model?

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What steps are involved in creating a new profit model called M3?

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What is the significance of the country name 'US' in the context of the lecture?

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What are the unique values of the column called 'state holiday' and what do they represent?

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What does the term 'dummy encoding' refer to in the context of adding regressors?

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Explain the difference between '1 hot encoding' and 'dummy encoding'.

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What is the purpose of the 'add regressor' function in the model?

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How does the model handle predictions when the store is closed or during holidays?

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What conclusions can be drawn from the results of the forecast and the plot components?

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