Prophet enables Python and R developers to build scalable time series forecasts. This course will help you implement Prophet's cutting-edge forecasting techniques to model future data with higher accuracy and with very few lines of code.
In this course, you will learn how to use Facebook Prophet to do time series analysis and forecasting. You will learn how the Prophet works under the hood (that is, what are its modeling assumptions?) and the Prophet API (that is, how to write the code).
This course is a practice-oriented course, demonstrating how to prepare your data for Prophet, fit a model, and use it to forecast, analyze the results, and evaluate the model's predictions. We will apply Prophet to a variety of datasets, including store sales and stock prices. You will learn how to use Prophet to plot the model's in-sample predictions and forecast. Then, learn how to plot the components of the fitted model. You will also learn how to deal with outliers, missing data, and non-daily (for example, monthly) data.
By the end of this course, you will be able to use Prophet confidently to forecast your data.
What You Will Learn
Prepare your data (a Pandas dataframe) for Facebook Prophet
Learn how to fit a Prophet model to a time series
Plot the components of the fitted model
Model holidays and exogenous regressors
Evaluate your model with forecasting metrics
Learn how to do changepoint detection with Prophet
Audience
Anyone interested in data science, machine learning, or who wishes to use time series analysis on their own data should take this course. Good Python programming skills are required, as well as knowledge of Pandas, Dataframes, and preferably some familiarity with Scikit-Learn, though this is not required.

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