Data Science - Time Series Forecasting with Facebook Prophet in Python - Prophet in Code: Data Preparation

The video tutorial guides viewers through setting up a Colab notebook for time series analysis using Prophet. It covers installing necessary libraries, downloading and exploring a dataset from Kaggle, and cleaning the data by focusing on sales when the store is open. The tutorial also explains how to prepare and format the data for modeling with Prophet, including setting the date as an index and ensuring the data is in chronological order.

What is the first step in setting up the Colab notebook for Prophet?

• Importing libraries

• Reading CSV

• Installing Prophet

• Downloading data

Which column in the dataset represents the time series data?

• Store ID

• Promo

• Sales

• Customers

What does the 'Promo' column indicate in the dataset?

• Sales amount

• Customer count

• Promotional events

• Store ID

Why are sales frequently zero in the dataset?

• The data is missing

• There are no customers

• There is no promotion

• The store is closed

What is the significance of setting the date as the index in the DataFrame?

• It makes plotting easier

• It is required for Prophet

• It helps in sorting the data

• It ensures unique dates

What is the purpose of renaming columns to 'Y' and 'DS'?

• To sort the data

• To remove missing data

• To simplify the DataFrame

• To match Prophet's requirements

How does Prophet handle missing data in the time series?

• It uses time as the only regressor

• It fills in the gaps automatically

• It requires regularly spaced intervals

• It ignores missing data