Time Series Forecasting with Facebook Prophet Online Course
Time Series Forecasting with Facebook Prophet Online Course
This course teaches you how to use Facebook Prophet for time series forecasting with Python and R. You’ll learn how Prophet works, including its assumptions and the Prophet API. Through hands-on practice, you’ll prepare data, fit models, and make accurate forecasts. The course covers real-world datasets like store sales and stock prices, and addresses challenges like outliers, missing data, and non-daily data. By the end, you’ll confidently use Prophet to make reliable time series predictions.
Who is this Course for?
- Ideal for those interested in data science, machine learning, or time series analysis.
- Requires good Python programming skills.
- Knowledge of Pandas and DataFrames is essential.
- Familiarity with Scikit-Learn is beneficial, but not mandatory.
What you will learn
- Prepare data (Pandas dataframe) for Facebook Prophet.
- Fit a Prophet model to a time series.
- Plot components of the fitted model.
- Model holidays and external regressors.
- Evaluate the model using forecasting metrics.
- Perform changepoint detection with Prophet.
Course Table of Contents
Welcome
- Introduction
- Outline
Time Series Basics
- Time Series Basics Section Introduction
- Forecasting Metrics
- The Naive Forecast and the Importance of Baselines
- Walk-Forward Validation
- Suggestion Box
Facebook Prophet
- How Does Prophet Work?
- Prophet: Code Preparation
- Prophet in Code: Data Preparation
- Prophet in Code: Fit, Forecast, Plot
- Prophet in Code: Holidays and Exogenous Regressors
- Prophet in Code: Cross-Validation
- Prophet in Code: Changepoint Detection
- Prophet: Multiplicative Seasonality, Outliers, Non-Daily Data
- (The Dangers of) Prophet for Stock Price Prediction
- Prophet Section Summary
