Master Time Series Forecasting in 60 Days
Develop a career-boosting skill without putting your life on hold
Let's go!
Are you tired of:
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Reading incomplete time series tutorials on Medium where you can't even reproduce the results?
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Browsing time series courses on Coursera only to find out that they are full of theory and exercises are in R?
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Looking for a good book on time series that is not too dry, with good practical examples and Python code?
Don't worry, I was in the same place
As a working data scientist, I use Python daily, but for some reason, everything about time series is in R.
I tried different courses, but all were too theoretical with manual derivations of equations. I don't want to prove a theorem, I want to solve business problems!
Plus, I didn't have the time or the means to go back to school and take a graduate program to learn time series forecasting.
Now, imagine this:
✅ Having a step-by-step process to master time series forecasting in 100% Python
✅ Being able to master time series forecasting on your own time, without putting your life and work on hold
✅ Completing real-life projects to add to your data science portfolio
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"As a professional data scientist, I was looking for a way to learn time series because I need it in my job. I had very limited knowledge on this subject. With this course, I learned everything I needed and it helped me a lot to succeed in my project. Marco is a great teacher! Thank you! I recommend it to everyone who want to learn about time series!"
Askia Khalid
Data Scientist
Introducing
Applied Time Series Forecasting in Python
The only step-by-step course that combines both statistical and deep learning methods for time series forecasting, all in Python.
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Fast path to mastery
Get on the fast track to master time series forecasting, from basics to the latest techniques.
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100% Python
The course uses only Python and TensorFlow! Use Jupyter or your favourite IDE as you follow along.
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Project-based
I believe in learning by doing. Complete more than 14 end-to-end forecasting projects throughout the course
Meet your instructor 👋
Hey, my name is Marco and I am your instructor and founder of Data Science with Marco.
My Background
I studied chemical engineering, but let's just say that wasn't my passion! I started learning web development on my own, landed my first job in the field, and studied data science at night. Self-learning data science was tough. I focused a lot on practical skills and building a portfolio of projects, which led me to my current role as a senior data scientist at one of Canada's largest banks.
Learning Time Series
There is a lot of time-dependent data in a bank, so I felt the need to learn time series forecasting. I had to translate R code to Python, debug bad online tutorials, and piece together many blog articles. The experience was so painful that I started sharing my learning through blog posts to make it easier for others to learn this new skill.
This led me to publishing a book with Manning Publications on time series! But I still had more to say on the subject, so I made this course! The absolute best way to learn time series forecasting. A complete course, easy to follow, with end-to-end projects, and 100% Python code.
Inside the course 🔎
Module 1: Introduction
We kick off the course with a gentle introduction to time series. We define them, explore their components and understand what there is a unique way to work with time series data. Then, we start forecasting with baseline models
Project 1: Forecast milk production
Module 2: The random walk
The random walk is a special case where we cannot make reasonable forecasts! Nonetheless, it is super important to understand the random walk process, as we explore fundamental concepts such as stationarity and autocorrelation.
Project 2: Analyze the daily closing price of Amazon (AMZN)
Module 3: Forecasting with the SARIMAX family of models
Things get interesting as we start working with the SARIMAX family of models! We first master the fundamental models AR(p) and MA(q), before combining them into the ARMA and ARIMA models. Then we add the ability to model seasonality with SARIMA, and add exogenous variables to our model using SARIMAX. We also design a general modeling procedure to be applied in any situation.
Project 3: Forecast a simulated MA process
Project 4: Forecast a simulated AR process
Project 5: Forecast a simulated ARMA process
Project 6: Forecast the quarterly electricity production
Project 7: Forecast the monthly milk production
Project 8: Forecast the monthly price of cattle
Module 4: Multivariate forecasting
In this module, we see how we can forecast more than one time series at a time, with the models VAR, VARMA and VARMAX. We also explore the concept of Granger causality, which is the basis of these models.
Project 9: Forecast the monthly price of cows and calves in Saskatchewan
Module 5: Exponential smoothing
Exponential smoothing is a forecasting procedure that is very fast, flexible and also powerful. In this module, we slowly build from simple exponential smoothing, to double and triple exponential smoothing.
Project 10: Forecast the monthly corticosteroid drug subsidy
Module 6: Dealing with multiple seasonal periods
Working with a single seasonal period is easy. But what if there is more than one? What is you data has a daily and a weekly seasonality? Then, we need to apply models like BATS or TBATS.
Project 11: Forecast the hourly traffic on the interstate
Module 7: Forecasting using decomposition
A unique forecasting approach where we forecast long-term and short-term effects and then combine them for a final prediction. This is done through the use of the Theta model.
Project 12: Forecast the weekly CO2 concentration
Module 8: Deep learning for time series forecasting
In this module, we apply different deep learning models for forecasting time series data. We implement a robust framework for any situation: single-step, multi-step or multivariate forecasting. We also implement early stopping and learning rate scheduling for the training of our models.
Project 13: Forecast the hourly load of an electricity transformer (ETT dataset)
BONUS 1: Prophet
Prophet is a popular forecasting library open sourced by Meta (formerly Facebook). This forecasting procedure is powerful and very easy to use. Knowing how to work with Prophet makes it very easy to work with any automated forecasting library.
Project 14: Forecast the hourly electricity consumption in a household
BONUS 2: State-of-the-art time series forecasting
In this module, we explore and implement the latest advances in time series forecasting. This module will be updated as new methods are designed and made available. Specifically, we explore N-BEATS (2020) and N_HiTS (2022).
Project 15: Forecast the daily minimum temperature
What my students say 💬
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"This course is really what I searched for: an intermediate course for time series. It's a good way to start with the review of the basics, because afterwards, everyone is on the recommended level. I would do another course from this instructor immediately. Nice job!"
Luis Kalckstein
Data Scientist
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"This is an amazing video series. The instructions are clear and concise, and the instructor is very knowledgeable. Great course."
Oscar Paulse
Data Scientist
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"This course goes straight to what you need to do to get things done! Pretty hard to find it out there."
Filipe Schenkel de Souza
Data Scientist
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"Very thorough and touches everything of importance."
Shankar Viswanath
AI/ML Developer
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"Clear explanations, great content, perfect pace."
Josip Nemet
Senior Business Manager
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"The instructor is an expert in time series concepts in Python. Must follow this course."
Mayurkumar Surani
Data Scientist
Enroll risk-free today!
I am convinced that you will love this course. Not entirely satisfied? You have 30 days to get your money back!
