Data Science - Time Series Forecasting with Facebook Prophet in Python - Prophet in Code: Fit, Forecast, Plot

The video tutorial explains how to use the Prophet library for time series forecasting. It covers data preparation, creating a Prophet instance, fitting the model, and making predictions. The tutorial also discusses analyzing forecast results, plotting forecasts, and refining the model by excluding days when the store is closed. Key components like trend, seasonality, and prediction intervals are explored, with a focus on improving model accuracy and interpretation.

What is the first step in using Prophet for time series forecasting?

• Creating a future data frame

• Fitting the model with data

• Plotting the forecast

• Analyzing prediction intervals

Why is daily seasonality disabled in the Prophet model for daily data?

• Because daily seasonality requires finer scale data

• To avoid overfitting the model

• Because the data is not daily

• To simplify the model

What does the 'periods' argument specify when creating a future data frame?

• The number of future steps to forecast

• The number of components in the model

• The number of past data points to include

• The frequency of the data

Which column in the forecast data frame represents the model's prediction?

• Trend

• Y hat

• Y hat lower

• Timestamp

What is a key limitation of the Prophet model as demonstrated in the forecast plot?

• It does not provide prediction intervals

• It requires human intervention for certain predictions

• It only works with monthly data

• It cannot predict trends accurately

Why is it beneficial to exclude days when the store is closed in the refined model?

• To include more holidays

• To simplify the model

• To improve prediction accuracy by avoiding zero sales

• To increase the number of data points

How does the refined model's prediction interval compare to the original model?

• It is no longer present

• It is wider and less accurate

• It is tighter and more accurate

• It remains the same