Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Save more on your purchases! discount-offer-chevron-icon
Savings automatically calculated. No voucher code required.
Arrow left icon
All Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Newsletter Hub
Free Learning
Arrow right icon
timer SALE ENDS IN
0 Days
:
00 Hours
:
00 Minutes
:
00 Seconds
Machine Learning for Time-Series with Python
Machine Learning for Time-Series with Python

Machine Learning for Time-Series with Python: Forecast, predict, and detect anomalies with state-of-the-art machine learning methods

Arrow left icon
Profile Icon Ben Auffarth
Arrow right icon
£9.99 per month
Full star icon Full star icon Full star icon Full star icon Empty star icon 4 (12 Ratings)
Paperback Oct 2021 370 pages 1st Edition
eBook
£32.99
Paperback
£41.99
Subscription
Free Trial
Renews at £9.99p/m
Arrow left icon
Profile Icon Ben Auffarth
Arrow right icon
£9.99 per month
Full star icon Full star icon Full star icon Full star icon Empty star icon 4 (12 Ratings)
Paperback Oct 2021 370 pages 1st Edition
eBook
£32.99
Paperback
£41.99
Subscription
Free Trial
Renews at £9.99p/m
eBook
£32.99
Paperback
£41.99
Subscription
Free Trial
Renews at £9.99p/m

What do you get with a Packt Subscription?

Free for first 7 days. $15.99 p/m after that. Cancel any time!
Product feature icon Unlimited ad-free access to the largest independent learning library in tech. Access this title and thousands more!
Product feature icon 50+ new titles added per month, including many first-to-market concepts and exclusive early access to books as they are being written.
Product feature icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Product feature icon Thousands of reference materials covering every tech concept you need to stay up to date.
Subscribe now
View plans & pricing

Key benefits

  • Explore popular and modern machine learning methods including the latest online and deep learning algorithms
  • Learn to increase the accuracy of your predictions by matching the right model with the right problem
  • Master time series via real-world case studies on operations management, digital marketing, finance, and healthcare

Description

The Python time-series ecosystem is huge and often quite hard to get a good grasp on, especially for time-series since there are so many new libraries and new models. This book aims to deepen your understanding of time series by providing a comprehensive overview of popular Python time-series packages and help you build better predictive systems. Machine Learning for Time-Series with Python starts by re-introducing the basics of time series and then builds your understanding of traditional autoregressive models as well as modern non-parametric models. By observing practical examples and the theory behind them, you will become confident with loading time-series datasets from any source, deep learning models like recurrent neural networks and causal convolutional network models, and gradient boosting with feature engineering. This book will also guide you in matching the right model to the right problem by explaining the theory behind several useful models. You’ll also have a look at real-world case studies covering weather, traffic, biking, and stock market data. By the end of this book, you should feel at home with effectively analyzing and applying machine learning methods to time-series.

Who is this book for?

This book is ideal for data analysts, data scientists, and Python developers who want instantly useful and practical recipes to implement today, and a comprehensive reference book for tomorrow. Basic knowledge of the Python Programming language is a must, while familiarity with statistics will help you get the most out of this book.

What you will learn

  • Understand the main classes of time series and learn how to detect outliers and patterns
  • Choose the right method to solve time-series problems
  • Characterize seasonal and correlation patterns through autocorrelation and statistical techniques
  • Get to grips with time-series data visualization
  • Understand classical time-series models like ARMA and ARIMA
  • Implement deep learning models, like Gaussian processes, transformers, and state-of-the-art machine learning models
  • Become familiar with many libraries like Prophet, XGboost, and TensorFlow

Product Details

Country selected
Publication date, Length, Edition, Language, ISBN-13
Publication date : Oct 29, 2021
Length: 370 pages
Edition : 1st
Language : English
ISBN-13 : 9781801819626
Category :
Languages :
Tools :

What do you get with a Packt Subscription?

Free for first 7 days. $15.99 p/m after that. Cancel any time!
Product feature icon Unlimited ad-free access to the largest independent learning library in tech. Access this title and thousands more!
Product feature icon 50+ new titles added per month, including many first-to-market concepts and exclusive early access to books as they are being written.
Product feature icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Product feature icon Thousands of reference materials covering every tech concept you need to stay up to date.
Subscribe now
View plans & pricing

Product Details

Publication date : Oct 29, 2021
Length: 370 pages
Edition : 1st
Language : English
ISBN-13 : 9781801819626
Category :
Languages :
Tools :

Packt Subscriptions

See our plans and pricing
Modal Close icon
£9.99 billed monthly
Feature tick icon Unlimited access to Packt's library of 6,500+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Simple pricing, no contract
£99.99 billed annually
Feature tick icon Unlimited access to Packt's library of 6,500+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Choose a DRM-free eBook or Video every month to keep
Feature tick icon PLUS own as many other DRM-free eBooks or Videos as you like for just £5 each
Feature tick icon Exclusive print discounts
£139.99 billed in 18 months
Feature tick icon Unlimited access to Packt's library of 6,500+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Choose a DRM-free eBook or Video every month to keep
Feature tick icon PLUS own as many other DRM-free eBooks or Videos as you like for just £5 each
Feature tick icon Exclusive print discounts

Frequently bought together


Stars icon
Total £ 123.97
Machine Learning with PyTorch and Scikit-Learn
£41.99
Modern Time Series Forecasting with Python
£39.99
Machine Learning for Time-Series with Python
£41.99
Total £ 123.97 Stars icon
Visually different images

Customer reviews

Top Reviews
Rating distribution
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
(12 Ratings)
5 star 41.7%
4 star 33.3%
3 star 16.7%
2 star 0%
1 star 8.3%
Filter icon Filter
Top Reviews

Filter reviews by




Trebor Jan 19, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The author has a great way of walking you through the content by starting with basic overview and then begins to delve into the details from preprocessing towards the models that are used then into the machine learning methods and models themselves. All while providing easy to read Python examples. Great for novice and advanced readers!
Amazon Verified review Amazon
WU. Mar 24, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
As close to a one-stop-shop for time series analysis in Python.Pedagogically, the author does an excellent job of walking the reader through the basics (time-series definition, preprocessing, python-specific packages, use cases, etc), to the classical models (ARCH, GARCH, Moving Average, Autoregressive, etc.), all the way to SOTA models using probabilistic techniques and RL. Throughout, he explains the output of the various packages and compares performance. Extra points for going over online-training and giving perhaps the most concise definitions of the different types of data drift I've read thus far.The accompanying code is clear and easy to follow, even when there's an occasional typo here or there.Highest recommendation!
Amazon Verified review Amazon
daniel yoo Jan 05, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Here are some of the major points I would like to point out in reading this book.1. The author created a very good reference manual for "everything" time series. He goes into talking about classical time series models and also talks about more novel approaches such as combining classical time series models with machine learning models . This stretched me to think about time series in a new and different way. (I have been working with classical time series for a long time).2. He provides solid coding examples with python packages that could help the reader immediately implement what they have learned in every chapter. This helps reinforce concepts, and ideas.3. The writing is very clear and fluid. Some historical context is given as well as reference to academic articles (original sources), where major concepts are summarized and made more palatable for the reader.Buy this book!
Amazon Verified review Amazon
Dwaraknaath Varadharajan Jan 09, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
What's new?The author has touched upon state of the art tools that exist today for preprocessing time series data and latest Machine Learning algorithms for time series such as ROCKET, Shapelets, Time Series Forest etc. There are a very few books dedicated to time series forecasting using Deep Learning, but this book has filled the void by covering a wide range of Deep Learning techniques that's been used in M4, M5 competitions.Summary:Overall, I think this book is pretty much like a literature review on recent advances in times series forecasting and readers will certainly get more than what they asked for.Suggestions:Chapters 1 and 2 covers basics of time series analysis and forecasting which can be found in many time series textbooks today. Time Series analysis could have been a little extensive by covering how lags, and rolling windows/fixed windows are useful. Even though I really liked the part where ROCKET and Shapelets are used to perform feature engineering, I think further explanation is required.
Amazon Verified review Amazon
Amazon Customer Feb 18, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This is one of the best books to study and understand about Time series with Machine Learning. The author has put a lot of effort get the master class material for the time series. This book covers all the libraries available for time series with different domains like Statistics, online learning, Machine learning, statistics, Deep Learning, and, Reinforcement learning.
Amazon Verified review Amazon
Get free access to Packt library with over 7500+ books and video courses for 7 days!
Start Free Trial

FAQs

What is included in a Packt subscription? Chevron down icon Chevron up icon

A subscription provides you with full access to view all Packt and licnesed content online, this includes exclusive access to Early Access titles. Depending on the tier chosen you can also earn credits and discounts to use for owning content

How can I cancel my subscription? Chevron down icon Chevron up icon

To cancel your subscription with us simply go to the account page - found in the top right of the page or at https://subscription.packtpub.com/my-account/subscription - From here you will see the ‘cancel subscription’ button in the grey box with your subscription information in.

What are credits? Chevron down icon Chevron up icon

Credits can be earned from reading 40 section of any title within the payment cycle - a month starting from the day of subscription payment. You also earn a Credit every month if you subscribe to our annual or 18 month plans. Credits can be used to buy books DRM free, the same way that you would pay for a book. Your credits can be found in the subscription homepage - subscription.packtpub.com - clicking on ‘the my’ library dropdown and selecting ‘credits’.

What happens if an Early Access Course is cancelled? Chevron down icon Chevron up icon

Projects are rarely cancelled, but sometimes it's unavoidable. If an Early Access course is cancelled or excessively delayed, you can exchange your purchase for another course. For further details, please contact us here.

Where can I send feedback about an Early Access title? Chevron down icon Chevron up icon

If you have any feedback about the product you're reading, or Early Access in general, then please fill out a contact form here and we'll make sure the feedback gets to the right team. 

Can I download the code files for Early Access titles? Chevron down icon Chevron up icon

We try to ensure that all books in Early Access have code available to use, download, and fork on GitHub. This helps us be more agile in the development of the book, and helps keep the often changing code base of new versions and new technologies as up to date as possible. Unfortunately, however, there will be rare cases when it is not possible for us to have downloadable code samples available until publication.

When we publish the book, the code files will also be available to download from the Packt website.

How accurate is the publication date? Chevron down icon Chevron up icon

The publication date is as accurate as we can be at any point in the project. Unfortunately, delays can happen. Often those delays are out of our control, such as changes to the technology code base or delays in the tech release. We do our best to give you an accurate estimate of the publication date at any given time, and as more chapters are delivered, the more accurate the delivery date will become.

How will I know when new chapters are ready? Chevron down icon Chevron up icon

We'll let you know every time there has been an update to a course that you've bought in Early Access. You'll get an email to let you know there has been a new chapter, or a change to a previous chapter. The new chapters are automatically added to your account, so you can also check back there any time you're ready and download or read them online.

I am a Packt subscriber, do I get Early Access? Chevron down icon Chevron up icon

Yes, all Early Access content is fully available through your subscription. You will need to have a paid for or active trial subscription in order to access all titles.

How is Early Access delivered? Chevron down icon Chevron up icon

Early Access is currently only available as a PDF or through our online reader. As we make changes or add new chapters, the files in your Packt account will be updated so you can download them again or view them online immediately.

How do I buy Early Access content? Chevron down icon Chevron up icon

Early Access is a way of us getting our content to you quicker, but the method of buying the Early Access course is still the same. Just find the course you want to buy, go through the check-out steps, and you’ll get a confirmation email from us with information and a link to the relevant Early Access courses.

What is Early Access? Chevron down icon Chevron up icon

Keeping up to date with the latest technology is difficult; new versions, new frameworks, new techniques. This feature gives you a head-start to our content, as it's being created. With Early Access you'll receive each chapter as it's written, and get regular updates throughout the product's development, as well as the final course as soon as it's ready.We created Early Access as a means of giving you the information you need, as soon as it's available. As we go through the process of developing a course, 99% of it can be ready but we can't publish until that last 1% falls in to place. Early Access helps to unlock the potential of our content early, to help you start your learning when you need it most. You not only get access to every chapter as it's delivered, edited, and updated, but you'll also get the finalized, DRM-free product to download in any format you want when it's published. As a member of Packt, you'll also be eligible for our exclusive offers, including a free course every day, and discounts on new and popular titles.