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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.

25 Days

Average time students take to complete this course.

instructor
Taught by: Diogo Resende
Last updated: April 2024

Course 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.

Join Zero To Mastery Now

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.

Join Zero To Mastery Now

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

6 lectures

Course Introduction5:03

PREVIEW

Exercise: Meet Your Classmates and Instructor

PREVIEW

Course Material2:12

PREVIEW

Why Forecasting Matters5:15

PREVIEW

Understanding Your Video Player (notes, video speed, subtitles + more)

PREVIEW

Set Your Learning Streak Goal

PREVIEW

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

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Python - Renaming Variable1:29

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Python - Summary Statistics2:33

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Additive vs. Multiplicative Seasonality2:40

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Python - Seasonal Decomposition8:18

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Python - Seasonal Graphs5:07

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Python - Visualization - Basic Plot5:31

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Python - Visualization - Customization5:58

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Python - Visualization -Adding Events6:10

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Python - Correlation2:18

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Auto-Correlation Plots2:01

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Python - Auto-Correlation Plot3:20

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Python - Useful Commands Template2:30

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Let's Have Some Fun (+ Free Resources)

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(Facebook) Prophet

25 lectures

Game Plan for Prophet1:29

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Prophet and Structural Time Series5:51

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Python - Preparing the Script3:24

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Python - Prepare Date Variable2:29

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Python - Easter Holiday4:29

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Python - Remaining Holidays2:36

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Python - Wrapping up the Events3:01

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Prophet Parameters2:30

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Python - Prophet Model5:39

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Cross-Validation3:16

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Python - Cross-Validation6:59

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Assessing Forecasting6:35

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Python - Cross-Validation Performance and Plotting9:30

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Parameter Tuning2:58

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Python - Parameter Grid4:37

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Python - Parameter Tuning7:29

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Python - Best Parameters and Exporting6:16

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Python - Updating Useful Commands (Part 1)1:32

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Python - Preparing Data Sets8:10

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Python - Parameters and Final Model9:25

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Python - Forecasting10:42

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Python - Exporting Forecasts4:41

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Python - Updating Useful Commands (Part 2)1:34

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Pros and Cons3:57

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Unlimited Updates

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SARIMAX

20 lectures

SARIMAX Game Plan1:52

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ARIMA3:05

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Python - Preparing Script3:29

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Auto-Regressive1:54

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Integrated4:33

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Python - Stationarity and Differencing5:44

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Moving Average Component2:34

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Optimization Factors3:15

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Python - SARIMAX Model5:11

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Python - Cross-Validation8:18

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Python - Parameter Grid3:32

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Python - Parameter Tuning4:14

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Python - Exporting Best Parameters4:26

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Python - Preparing the Script3:27

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Python - Preparing Data2:56

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Python - Tuned SARIMAX Model4:02

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Python - Forecasting4:28

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Python - Visualization and Export3:54

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SARIMAX Pros and Cons1:48

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Course Check-In

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How LinkedIn Silverkite Works

35 lectures

LinkedIn Silverkite Game Plan1:35

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LinkedIn Silverkite3:08

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Silverkite vs. Prophet3:11

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Python - Libraries and Data10:05

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Python - Preparing Data3:36

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Python - Metadata2:47

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Silverkite Components4:20

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Growth Terms1:56

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Python - Growth Terms2:04

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Seasonality Terms3:20

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Python - Seasonality2:06

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Python - Available Countries and Holidays3:31

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Python - Holidays6:21

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Python - Changepoints1:22

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Python - Regressors1:15

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Lagged Regressors1:54

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Python - Lagged Regressors1:37

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Python - Autoregression2:19

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Fitting Algorithms Possibilities2:38

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Ridge Regression7:52

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XGBoost3:44

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Boosting7:03

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Feature Sampling2:56

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Python - Custom Fit Algorithm2:12

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Python - Silverkite Model2:48

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Python - Cross-Validation Configuration8:27

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Python - SIlverkite Parameter Tuning6:01

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Python - Visualization and Preparing Results8:01

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Python - Exporting Best Parameters6:06

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Python - Preparing Script3:17

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Python - Tuned Silverkite Model6:03

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Python - Summary and Visualization11:16

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Python - Forecasting and Exporting3:49

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Pros and Cons2:09

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Implement a New Life System

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

22 lectures

Game Plan for LSTM2:05

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Simple Neural Network4:55

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Recurrent Neural Networks (RNN)3:01

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Long Short-Term Memory (LSTM)4:11

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Python - Directory and Libraries2:47

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Python - Time Series Objects2:56

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Python - Time Variables6:45

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Python - Scaling5:45

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LSTM Parameters2:57

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Activation Functions6:03

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Python - LSTM Model6:54

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Python - Cross-Validation5:03

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Python - Cross-Validation Performance7:33

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Python - Parameter Grid4:43

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Python - Parameter Tuning (Round 1)5:18

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Python - Parameter Tuning (Round 2)9:37

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Python - Changing from CPU to GPU3:59

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Python - Parameter Tuning (Final Results)2:26

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Python - Preparing Script4:57

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Python - Tuned LSTM Model8:52

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Python - Predictions and Exporting3:50

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Pros and Cons2:10

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Ensemble

7 lectures

Ensemble Game Plan1:21

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Ensemble Mechanism4:43

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Python - Preparing Script and Loading Predictions7:23

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Python - Loading Errors5:02

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Python - Forecasting Weights4:55

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Python - Ensemble Forecast and Visualization3:31

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Ensemble Pros and Cons2:17

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Where To Go From Here?

6 lectures

Thank You!1:17

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Review This Course!

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Become An Alumni

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Learning Guideline

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ZTM Events Every Month

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LinkedIn Endorsements

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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.

Are there subtitles?

Yes! We have high quality subtitles in 11 different languages: English, Spanish, French, German, Dutch, Romanian, Arabic, Hindi, Portuguese, Indonesian, and Japanese.

You can even adjust the text size, color, background and more so that the subtitles are perfect just for you!

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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