Data Science - Time Series Forecasting with Facebook Prophet in Python

Description

In this compact intermediate-level course, you will learn how to use Facebook Prophet to do time series analysis and forecasting. You will learn how Prophet works under the hood and the Prophet API. We will apply Prophet to a variety of datasets, including store sales and stock prices.

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.

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

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.

This course is a learn-by-doing course where you will learn how to prepare your data for Prophet, fit a model, and use it to forecast, analyze the results, and evaluate the model's predictions. The course is well-balanced with both theoretical and practical coding exercises. In each section, we first cover the theory concept and demonstrate it using a real-world example for better understanding.

Teaches how to make a forecast using Prophet * Explains how to use Prophet to predict stock prices * Covers how to use Prophet to plot the model's in-sample predictions and forecast

The Lazy Programmer is an AI/ML engineer focusing on deep learning with experience in data science, big data engineering, and full-stack development. With a background in computer engineering and specialization in ML, he holds two master's degrees in computer engineering and statistics with finance applications. His online advertising and digital media expertise include data science and big data. He has created DL models for prediction and has experience in recommender systems using reinforcement learning and collaborative filtering. He is a skilled instructor who has taught at universities including Columbia, NYU, Hunter College, and The New School. He is a web programmer with experience in Python, Ruby/Rails, PHP, and Angular.

1. Welcome

Welcome to the course! In this section, we will get introduced to the course goals.

2. Time Series Basics

In this section, we will talk about time series basics.

3. Facebook Prophet

In this section, we will explore Facebook Prophet.