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, certificate of completion (shareable on LinkedIn, Facebook, and Twitter), Q&A forum

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

Testimonials and Success Stories

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Looking forward to your new stuff.

Thank you for doing this! I wish everyone who call’s themselves a Data Scientist would take the time to do this either as a refresher or learn the material. I have had to work with so many people in prior roles that wanted to jump right into machine learning on my teams and didn’t even understand the first thing about the basics you have in here!!

I am signing up so that I have the easy refresh when needed and the see what you consider important, as well as to support your great work, thank you.

Thank you, I think you have opened my eyes. I was using API to implement Deep learning algorithms and each time I felt I was messing out on some things. So thank you very much.

I have been intending to send you an email expressing my gratitude for the work that you have done to create all of these data science courses in Machine Learning and Artificial Intelligence. I have been looking long and hard for courses that have mathematical rigor relative to the application of the ML & AI algorithms as opposed to just exhibit some 'canned routine' and then viola here is your neural network or logistical regression. ...


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In the process of completing my Master’s at Hunan University, China, I am writing this feedback to you in order to express my deep gratitude for all the knowledge and skills I have obtained studying your courses and following your recommendations.

The first course of yours I took was on Convolutional Neural Networks (“Deep Learning p.5”, as far as I remember). Answering one of my questions on the Q&A board, you suggested I should start from the beginning – the Linear and Logistic Regression courses. Despite that I assumed I had already known many basic things at that time, I overcame my “pride” and decided to start my journey in Deep Learning from scratch. ...


By the way, if you are interested to hear. I used the HMM classification, as it was in your course (95% of the script, I had little adjustments there), for the Customer-Care department in a big known fintech company. to predict who will call them, so they can call him before the rush hours, and improve the service. Instead of a poem, I Had a sequence of the last 24 hours' events that the customer had, like: "Loaded money", "Usage in the food service", "Entering the app", "Trying to change the password", etc... the label was called or didn't call. The outcome was great. They use it for their VIP customers. Our data science department and I got a lot of praise.



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 (05:49)
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