Data Science: Time Series Forecasting with Facebook Prophet in Python

Time Series Analysis with the Facebook Prophet Library

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Course Data

Lectures: 22
Length: 2h 47m
Skill Level: All Levels
Languages: English
Includes: Lifetime access

Course Description

In this self-paced course, you will learn how to use Facebook Prophet to do Time Series Analysis and Forecasting. You will learn about how Prophet works under the hood (i.e. what are its modeling assumptions?) and the Prophet API (i.e. 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. The course includes video presentations, coding lessons, hands-on exercises, and links to further resources.

This course is intended for:
  • Anyone interested in data science and machine learning
  • Students and professionals who want to apply time series analysis to their own data
Suggested prerequisites:
  • Decent Python programming skill
  • Familiarity with Pandas and Dataframes
  • Experience with scikitlearn is useful but not necessary
In this course, we will cover:
  • how to prepare your data (a Pandas dataframe) for Facebook Prophet
  • how to fit a Prophet model to a time series
  • how to make a forecast using Prophet
  • how to use Prophet to plot the model's insample predictions and forecast (with prediction intervals)
  • how to plot the components of the fitted model (trend, error, and seasonal components)
  • how to model holidays and exogenous regressors
  • how to evaluate your model with forecasting metrics (MSE, RMSE, MAE, MAPE, sMAPE)
  • how to perform crossvalidation (walkforward validation) with Prophet
  • baselines, the random walk hypothesis, and the naive forecast
  • how to do changepoint detection with Prophet
  • how to model multiplicative seasonality with Prophet
  • how to deal with outliers and missing data
  • how to deal with nondaily (e.g. monthly) data
  • how to use Prophet to predict stock prices

Lectures

Welcome

3 Lectures · 08min
  1. Introduction (03:16) (FREE preview available)
  2. Outline (03:17)
  3. Where to get the code and data - instant access (01:42)

Time Series Basics

4 Lectures · 33min
  1. Time Series Basics Section Introduction (06:22)
  2. Forecasting Metrics (10:44)
  3. The Naive Forecast and the Importance of Baselines (08:47)
  4. Walk-Forward Validation (07:42)

Facebook Prophet

11 Lectures · 01hr 29min
  1. Prophet Section Introduction (03:11)
  2. How does Prophet work? (08:24)
  3. Prophet: Code Preparation (12:41)
  4. Prophet in Code: Data Preparation (08:59)
  5. Prophet in Code: Fit, Forecast, Plot (08:30)
  6. Prophet in Code: Holidays and Exogenous Regressors (10:19)
  7. Prophet in Code: Cross-Validation (06:07)
  8. Prophet in Code: Changepoint Detection (04:14)
  9. Prophet: Multiplicative Seasonality, Outliers, Non-Daily Data (10:16)
  10. (The Dangers of) Prophet for Stock Price Prediction (13:10)
  11. Prophet Section Summary (03:27)

Effective Learning Strategies for Machine Learning (Appendix/FAQ by Student Request)

2 Lectures · 27min
  1. What order should I take your courses in? (part 1) (11:19)
  2. What order should I take your courses in? (part 2) (16:07)

Appendix / FAQ Finale

2 Lectures · 08min
  1. What is the Appendix? (02:48)
  2. Where to get discount coupons and FREE deep learning material (05:31)
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