Data Science: Time Series Forecasting with Facebook Prophet in Python
Time Series Analysis with the Facebook Prophet Library
Data Science,Python,Development
Lectures -19
Duration -2 hours
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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 scikit-learn 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 in-sample 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 cross-validation (walk-forward 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 non-daily (e.g. monthly) data
- how to use Prophet to predict stock prices
Goals
What will you learn in this course:
- 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 in-sample 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 cross-validation (walk-forward 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 non-daily (e.g. monthly) data
- how to use Prophet to predict stock prices
Prerequisites
What are the prerequisites for this course?
- Decent Python programming skill
- Familiarity with Pandas and Dataframes
- Experience with scikit-learn is useful but not necessary
Curriculum
Check out the detailed breakdown of what’s inside the course
Welcome
4 Lectures
- Introduction 03:15 03:15
- Outline 03:16 03:16
- Connect With Me For FREE Data Science & Machine Learning Tutorials 00:59 00:59
- Resources
Time Series Basics
5 Lectures
Facebook Prophet
10 Lectures
Instructor Details
Lazy Programmer
The Lazy Programmer is a seasoned online educator with an unwavering passion for sharing knowledge. With over 10 years of experience, he has revolutionized the field of data science and machine learning by captivating audiences worldwide through his comprehensive courses and tutorials.
Equipped with a multidisciplinary background, the Lazy Programmer holds a remarkable duo of master's degrees. His first foray into academia led him to pursue computer engineering, with a specialized focus on machine learning and pattern recognition. Undeterred by boundaries, he then ventured into the realm of statistics, exploring its applications in financial engineering.
Recognized as a trailblazer in his field, the Lazy Programmer quickly embraced the power of deep learning when it was still in its infancy. As one of the pioneers, he fearlessly embarked on instructing one of the first-ever online courses on deep learning, catapulting him to the forefront of the industry.
Beyond the realm of education, the Lazy Programmer possesses invaluable hands-on experience that has shaped his expertise. His ventures into online advertising and digital media have yielded astounding results, propelling click-through rates and conversion rates to new heights and boosting revenues by millions of dollars at the companies he's worked for. As a full-stack software engineer, he boasts intimate familiarity with an array of backend and web technologies, including Python, Ruby on Rails, C++, Scala, PHP, Javascript, SQL, big data, Spark, and Redis.
While his achievements in the field of data science and machine learning are awe-inspiring, the Lazy Programmer's intellectual curiosity extends far beyond these domains. His fervor for knowledge leads him to explore diverse fields such as drug discovery, bioinformatics, and algorithmic trading. Embracing the challenges and intricacies of these subjects, he strives to unravel their potential and contribute to their development.
With an unwavering commitment to his students and a penchant for simplifying complex concepts, the Lazy Programmer stands as an influential figure in the realm of online education. Through his courses in data science, machine learning, deep learning, and artificial intelligence, he empowers aspiring learners to navigate the intricate landscapes of these disciplines with confidence.
As an author, mentor, and innovator, the Lazy Programmer leaves an indelible mark on the world of data science, machine learning, and beyond. With his ability to demystify the most intricate concepts, he continues to shape the next generation of data scientists and inspires countless individuals to embark on their own intellectual journeys.
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