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Time Series Forecasting with Python

This project-based course will put you in the role of a Business Data Analyst at Airbnb tasked with predicting demand for Airbnb property bookings in New York. To accomplish this goal, you'll use the Python programming language to build a powerful tool that utilizes the magic of time series forecasting.

instructor

Taught by: Diogo Resende

Last updated: March 2023

Project overview

The world is changing. Every decision must be made faster, smarter, and more accurately. And that's possible with business data analysis. You'll learn new skills and put them to the test in this project that puts you in the hypothetical scenario of a Business Data Analyst at Airbnb tasked with predicting demand for Airbnb properties in New York. You'll use Python to build a tool utilizing time series forecasting to help Airbnb make the best, most accurate decision, and have a portfolio project that you can use to impress employers.

What you'll learn

  • How to utilize the power of time series forecasting to predict the future
  • How to use the four most relevant forecasting models used by Business Data Analysts today
  • Practice the day-to-day skills needed for Business Data Analysis
  • Build an impressive project to add to your portfolio to help you get hired
  • Enhance your proficiency with Python, one of the most popular programming languages

Let's start at the beginning: What is Time Series Forecasting?

A time series is where you analyze data in consecutive time periods, such as days, weeks, years, business days, or really any time period. Taking the impact of time into account is key in successful data analysis, and time series help you understand that and use it to your advantage.

Forecasting you've probably heard about before. The weatherperson forecasts the weather for you every day. What you're doing when forecasting is trying to predict the future as accurately as possible.

So time series forecasting is really about using time series data and analysis to forecast what the future is likely to look like.

Sounds pretty useful, right?

Well, just like the weatherperson, you might not be able to perfectly predict the future, but with the right data, tools, and analysis you can make sure your prediction is the best it can be and be able to rely on your predictions with a high degree of trust.

Why Should I Learn By Building A Project?

Building a project is one of the best ways to learn. At ZTM, we're all about learning by doing. It'll teach you how to actually work in the real world and not to shy away from the hard stuff.

Plus to land your dream job you're going to need portfolio projects that you can use to show off your skills to employers. This project will be a great addition to your portfolio that you can use to wow your future employers and land your dream job.

What Will I Learn In This Project?

With this project you'll learn the four most relevant forecasting models used by Business Data Analysts today including the latest tools needed to utilize them.

But there's more. Through this project you'll also learn about:

1. Facebook Prophet: a tool developed by Meta (Facebook) for producing reliable forecasts that assist with planning and setting business goals.

2. SARIMAX Modeling in Python: what is SARIMAX? Well it stands for "Seasonal Auto-Regressive Integrated Moving Average with eXogenous" factors, which we know is a mouthful (thank goodness for acronyms!). All you need to know for now is that SARIMAX is a great tool for time series analysis and is part of the ARIMA family of models.

3. LinkedIn Silverkite: LinkedIn has developed a top forecasting model as well, called Silverkite! This is arguably even better than Facebook Prophet, though each have their uses. It uses automated modeling to make forecasts better and more accurate.

4. Recurrent Neural Networks: learn and utilize cutting-edge neural networks as they are applied to time series, including Long Short-Term Memory, a deep learning forecasting model that is highly marketable and sought-after!

5. The Ensemble Methodology: a common forecasting methodology that combines forecasts from multiple models to improve accuracy.

One of the best parts of this project (and all ZTM courses) is that you won't be learning alone.

By enrolling today, you’ll also get to join our exclusive live online community classroom to learn alongside thousands of students, alumni, mentors, TAs and Instructors.

How well can you use time series forecasting to predict the future? Click Start Learning Now to join the Academy and start building this portfolio project to find out. We'll see you inside!

When's the best time to get started? Today!

There's never a bad time to learn in-demand skills. But the sooner, the better. So start learning Time Series Forecasting today by joining the ZTM Academy. You'll have a clear roadmap to developing the skills to build your own projects, get hired, and advance your career.

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Expand Overview

What you'll build

The best way you learn is by doing. Not just watching endless tutorials. That's why a key part of this course is the real-world project that you'll get to build. Plus it'll look great on your portfolio.

Airbnb Forecasting Product for New York City

Airbnb Forecasting Product for New York City

In this project Airbnb is looking for you to improve the accuracy of its forecasts of daily demand for its New York properties. To achieve this you'll build a state-of-the-art forecasting product that utilizes KPIs and helps Airbnb grow its business.

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Course Curriculum

To make sure this course is a good fit for you, you can start learning Time Series Forecasting with Python for free right now by clicking any of the PREVIEW links below.

Introduction

3 lectures

Byte Sized Learning

PREVIEW

Exercise: Meet Your Classmates and Instructor

START

How-to's: Speed up videos, Downloading videos, Subtitles

START

Section 1: The Anatomy of a Forecasting Product

3 lectures

Course Introduction5:03

PREVIEW

Course Material2:12

PREVIEW

Why Forecasting Matters5:15

PREVIEW

Section 2: Exploratory Data Analysis

18 lectures

Game Plan1:04

PREVIEW

TIme Series Data2:34

PREVIEW

Case Study Briefing1:43

PREVIEW

Python - Directory and Libraries4:42

PREVIEW

Python - Loading the Data2:51

PREVIEW

Python - Renaming Variable1:29

START

Python - Summary Statistics2:33

START

Additive vs. Multiplicative Seasonality2:40

START

Python - Seasonal Decomposition8:18

START

Python - Seasonal Graphs5:07

START

Python - Visualization - Basic Plot5:31

START

Python - Visualization - Customization5:58

START

Python - Visualization -Adding Events6:10

START

Python - Correlation2:18

START

Auto-Correlation Plots2:01

START

Python - Auto-Correlation Plot3:20

START

Python - Useful Commands Template2:30

START

Unlimited Updates

START

Section 3: Facebook Prophet

23 lectures

Facebook Prophet Game Plan1:36

START

Structural Time Series and Facebook Prophet4:42

START

Python - Preparing the Script3:50

START

Python - Date Variable2:04

START

Python - Easter4:08

START

Python - Thanksgiving1:24

START

Python - Wrapping Up the Events2:32

START

Facebook Prophet Parameters2:04

START

Facebook Prophet Model3:49

START

Cross-Validation3:48

START

Python - Cross-Validation5:29

START

Assessing Model Errors4:37

START

Python - Cross-Validation Performance and Plot7:52

START

Parameter Tuning1:50

START

Python - Parameter Grid4:47

START

Python - Parameter Tuning6:56

START

Python - Best Parameters and Exporting6:55

START

Python - Building Script5:06

START

Python - Preparing Data Sets5:54

START

Python - Final Facebook Prophet Model7:04

START

Python - Forecasting7:36

START

Python - Exporting Forecast5:15

START

Facebook Prophet Pros and Cons1:40

START

Section 4: SARIMAX

19 lectures

SARIMAX Game Plan1:52

START

ARIMA3:05

START

Python - Preparing Script3:29

START

Auto-Regressive1:54

START

Integrated4:33

START

Python - Stationarity and Differencing5:44

START

Moving Average Component2:34

START

Optimization Factors3:15

START

Python - SARIMAX Model5:11

START

Python - Cross-Validation8:18

START

Python - Parameter Grid3:32

START

Python - Parameter Tuning4:14

START

Python - Exporting Best Parameters4:26

START

Python - Preparing the Script3:27

START

Python - Preparing Data2:56

START

Python - Tuned SARIMAX Model4:02

START

Python - Forecasting4:28

START

Python - Visualization and Export3:54

START

SARIMAX Pros and Cons1:48

START

Section 5: How LinkedIn Silverkite Works

34 lectures

LinkedIn Silverkite Game Plan1:35

START

LinkedIn Silverkite3:08

START

Silverkite vs. Prophet3:11

START

Python - Libraries and Data10:05

START

Python - Preparing Data3:36

START

Python - Metadata2:47

START

Silverkite Components4:20

START

Growth Terms1:56

START

Python - Growth Terms2:04

START

Seasonality Terms3:20

START

Python - Seasonality2:06

START

Python - Available Countries and Holidays3:31

START

Python - Holidays6:21

START

Python - Changepoints1:22

START

Python - Regressors1:15

START

Lagged Regressors1:54

START

Python - Lagged Regressors1:37

START

Python - Autoregression2:19

START

Fitting Algorithms Possibilities2:38

START

Ridge Regression7:52

START

XGBoost3:44

START

Boosting7:03

START

Feature Sampling2:56

START

Python - Custom Fit Algorithm2:38

START

Python - Silverkite Model2:48

START

Python - Cross-Validation Configuration8:27

START

Python - SIlverkite Parameter Tuning6:01

START

Python - Visualization and Preparing Results8:01

START

Python - Exporting Best Parameters6:06

START

Python - Preparing Script3:33

START

Python - Best Parameters and Silverkite Model7:51

START

Python - Summary and Visualization6:26

START

Python - Exporting Forecasts3:06

START

Pros and Cons2:09

START

Section 6: Recurrent Neural Networks (RNN) Long Short-Term Memory (LSTM)

21 lectures

Recurrent Neural Networks (RNN) Long Short-Term Memory (LSTM) Game Plan2:13

START

Simple Neural Network5:58

START

Recurrent Neural Networks (RNN)3:27

START

Long Short-Term Memory (LSTM)5:31

START

Python - Libraries and Data5:20

START

Python - Time Series Objects4:55

START

Python - Time Variables9:01

START

Python - Scaling Variables9:03

START

LSTM Parameters2:14

START

Python - LSTM Model8:57

START

Python - Cross-Validation4:22

START

Python - CV Performance10:21

START

Python - Parameter Grid4:21

START

Python - Parameter Tuning (Round 1)7:18

START

Python - Parameter Tuning (Round 2)6:42

START

Python - Parameter Tuning (Final Results)2:28

START

Python - Preparing Script3:59

START

Python - Preparing Inputs3:47

START

Python - Tuned LSTM Model4:17

START

Python - Predictions and Exporting4:04

START

LSTM Pros and Cons2:20

START

Section 7: Ensemble

7 lectures

Ensemble Game Plan1:21

START

Ensemble Mechanism4:43

START

Python - Preparing Script and Loading Predictions7:23

START

Python - Loading Errors5:02

START

Python - Forecasting Weights4:55

START

Python - Ensemble Forecast and Visualization3:31

START

Ensemble Pros and Cons2:17

START

Where To Go From Here?

6 lectures

Thank You!

START

Review This Course!

START

Become An Alumni

START

Learning Guideline

START

LinkedIn Endorsements

START

Coding Challenges

START

Frequently asked questions

Your instructor isn't just an expert with years of real-world professional experience. He has been in your shoes. He makes learning fun. He makes complex topics feel simple. He will motivate you. He will push you. And he will go above and beyond to help you succeed.

Diogo Resende

Hi, I'm Diogo Resende!

Diogo has been working for over a decade as a data scientist. He loves harnessing the power of data and analytics to understand what has happened, what will happen next, and how to use that information to your advantage.

SEE MY BIO & COURSES

Diogo Resende

Data Scientist

Frequently asked questions

Are there any prerequisites for this course?

Who is this course for?

  • Anyone who wants to become a Business Data Analyst
  • Existing Business Data Analysts that want to practice and improve their skills
  • Developers looking to utilize their Python knowledge in new ways
  • Programmers that want to skillstack
  • Anyone with an interest in learning how to make data-driven decisions

Do you provide a certificate of completion?

We definitely do and they are quite nice. You will also be able to add Zero To Mastery Academy to the education section of your LinkedIn profile as well.

Can I download the videos?

Definitely. You can download any and all lessons for personal use. We do everything we can to make learning easy, fun and accessible. Whether that’s on your commute, on a flight or just when you have limited access to good WiFi.

Still have more questions about the Academy?

Still have more questions specific to the Academy membership? No problem, we answer some more here.

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