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From the Publisher

Create models for seasonal effects and external variables
Multivariate forecasting models to predict multiple time series
Deep learning for large datasets
Automate the forecasting process
Immediately applicable concepts that yield tangible results
100% Python code

Why read this book?

Real-world applications of time series forecasting span a wide range of fields and industries including supply chain management, econometrics, retail forecasting, environmental studies, and healthcare, among many others.

In Time Series Forecasting in Python, you’ll learn to build powerful predictive models from time-based data, starting with the basics and quickly advancing to developing large-scale models using Python, TensorFlow, and other modern deep learning tools.

Imagine…

How a business could benefit from foreseeing economic trends
How much damage could be prevented if we knew when a natural disaster was likely to strike
How many lives could be saved if the progress of a disease could be predicted

Book Highlights

In chapter 2, learn how naïve forecasting helps guide you as you take a random walk to determine whether a particular stock would be a profitable investment.
In chapter 8, put the S in SARIMA to detect, analyze, and forecast seasonal patterns in the total number of monthly passengers for an airline.
In chapter 19, predict the popularity of the search term “chocolate” on Google in an exercise that helps marketing teams sweeten their ROI.

What approach does this book take that is different to other books on this subject?

Time Series Forecasting in Python stands out from other books on this subject by showcasing how Python can be used for both machine learning and statistical approaches to time series analysis.

While many time series forecasting solutions have been implemented in R (a language made for statistical computing), data scientists are generally proficient in Python: the most predominant language in the machine learning field.

Time Series Forecasting in Python stands out compared to other books on this subject as it showcases how modern technology such as TensorFlow and Python can be used for time series analysis.

Thanks to Python’s versatility, we can develop models, perform statistical tests, and develop a web interface using the same programming language.

This book is written for data scientists or analysts with at least one year of experience, perhaps already working in R and looking to transition to Python. No prior experience in time series analysis is required.

Editorial Reviews

Review

"The importance of time series analysis cannot be overstated. This book provides key techniques to deal with time series data in real-world applications. Indispensable."
—Amaresh Rajasekharan, IBM

"Marco Peixeiro presents concepts clearly using interesting examples and illustrative plots. You'll be up and running quickly using the power of Python."
—Ariel Andres, MD Financial Management

"What caught my attention were the practical examples immediately applicable to real life. He explains complex topics without the excess of mathematical formalism."
—Simone Sguazza, University of Applied Sciences and Arts of Southern Switzerland

"A superb book on all things around modeling and predicting with time series data."
- Gary Bake, Data Scientist, Brambles

"This book will teach you everything you need to know on time series"
- David Paccoud, Principal Architect at Clario

From the Author

With this book, I hope to create the one-stop reference for time series forecasting with Python. It covers both statistical and machine learning models. We also work with automated forecasting libraries, as they are widely used in the industry and often act as baseline models. The book greatly emphasizes on a hands-on, practical approach, with various real-life scenarios. In real life, data is messy, dirty, sometimes missing, and I so I wanted to give the reader a safe space to experiment with those difficulties, learn from them, and easily transpose those skills in their own projects.
In each chapter, you will find exercises to practice and hone your skills. Each exercise comes with a full solution on GitHub. I highly suggest that you take the time to complete them, as you will gain important practical skills. It is a great way to test your knowledge, see what you need to revise in a given chapter, and apply modeling techniques in new scenarios.
Upon reading and completing the exercises, readers will have all the necessary tools to tackle any forecasting project with confidence and great results. Hopefully, they will also gain the curiosity and motivation to go beyond this book and become a time series expert.

Product details

Publisher ‏ : ‎ Manning (October 4, 2022)
Language ‏ : ‎ English
Paperback ‏ : ‎ 456 pages
ISBN-10 ‏ : ‎ 161729988X
ISBN-13 ‏ : ‎ 978-1617299889
Item Weight ‏ : ‎ 1.77 pounds
Dimensions ‏ : ‎ 7.38 x 1.1 x 9.25 inches

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Marco Peixeiro

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Senior data scientist , author, published researcher, blogger and online instructor! I hope to inspire my students to keep learning about data science and ultimately become better data scientists. Find more on datasciencewithmarco.com

Customer reviews

4.4 out of 5

22 global ratings

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Arman

Very practical book

Reviewed in the United States on December 9, 2023

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Great book that starts with very basic things and goes all the way to the very sophisticated concepts. It is very easy to follow, especially python code is clearly explained, almost line by line. After running examples of code for deep neural networks decided that I want to learn TensorFlow and ordered the next book from the same publisher.

Helpful

CY Li

introduce the topics ranging from classical methods to the latest machine learning techniques

Reviewed in the United States on December 20, 2022

It has covered the latest topics about applying machine learning in time series forecasting. It also briefly explains and illustrates many classical methods in Python. A very good book of introduction to learn time series forecast.

2 people found this helpful

Very good book for the beginners to intermediate

Reviewed in the United States on June 9, 2023

This is a very good book that takes the topics of time series for beginners to intermediate in a very accurate and explains things in very simple manner, this will help non technical folks get a good introduction to this topic. In summary very good book.

Alex Gilgur

It’s an OK text covering the basics

Reviewed in the United States on May 23, 2023

The jump to ML is done way too early

rvat119

great book on time series forecasting

Reviewed in the United States on October 20, 2022

The structure of the book to get one started with concepts, thinking about the time series problem, and analysis makes this unique. Most of the books go after demonstrating the python code rather than trying to put the concepts together and using python examples to illustrate the concepts. The flow charts for decision making are nice and mathematical concepts are clearly explained. Great Book.

One person found this helpful

Señor Gato

Does not actually show you how to forecast.

Reviewed in the United States on January 29, 2023

This book explains the concepts in a non-mathematical way. So, if you do not have a mathematical background then you should be able to follow along. Codes in the book sometimes do not work but if you look at the github page for the book, these errors have been corrected.
My biggest problem with the book is that it does not show you how to forecast beyond your dataset. For example, if you have a monthly/weekly dataset ranging from 2010-2022, then this book does not show you how to forecast into the year 2023. All the models (at least the classic models that the book has talked about, I haven't looked at the deep learning ones yet) divide the dataset into training and test data and then show you how the model is performing against the test data. Now, that we know the models are performing well, how about forecasting beyond the test dataset? That part is missing in the book. So, in my opinion the book helps you to understand forecast theory in a non-mathematical way but does not actually show you how to forecast past the test data that you have available. Another negative is that the book repeats a lot of stuff. It seems like the author is justifying the high price of the book with the number of pages (at close to sixty dollars, this book is pretty expensive for what it is trying to teach). So, two stars for the theoretical part and took away three stars for not showing how to forecast past the test dataset.
When someone buys a book on forecast, they normally hope to learn how to forecast into the future and not just learn how forecasting works within their available dataset. This book fails miserably in this particular direction.

7 people found this helpful

Zach Oakes

Good book, great for beginners — hoped for slightly more

Reviewed in the United States on October 6, 2022

It’s a solid book, excellent introduction, and I definitely have a far greater understanding of the various models, and reasoning behind their components.
I had hoped for a bit more, but I think the time series model family is just fairly simple in concept and practice — I should be thrilled it’s so simple, but yeah guess I wanted more of an intermediate read.
I suppose I feel like I didn’t add much to my toolkit, more so just have a greater understanding of them — hoped for a bit of both.
I also bought this pre release when index wasn’t fully formed, so had I read the full index I probably would have realized this pre buy — the fault lies with me there.

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joseanquiles

El mejor libro para empezar con las series de tiempo

Reviewed in Spain on April 9, 2024

El autor explica todo muy bien. Empieza con ejemplos muy sencillos y los va complicando poco a poco. No llegas a perderte, porque el autor tiene la habilidad de ir paso a paso.

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Agustin Alonso-Rodriguez

Time Series Forecasting in Python

Reviewed in Spain on April 18, 2023

Excelente libro, muy claro, completo y actualizado.

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