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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

What do you get with Print?

Instant access to your digital copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Redeem a companion digital copy on all Print orders

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

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Frequently bought together

Modern Time Series Forecasting with Python

€39.99

Machine Learning for Time-Series with Python

€41.99

Machine Learning with PyTorch and Scikit-Learn

€41.99

Total € 123.97

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Amazon verified reviews

Trebor Jan 19, 2022

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

WU. Mar 24, 2022

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!

Dwaraknaath Varadharajan Jan 09, 2022

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.

daniel yoo Jan 05, 2022

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 Customer Feb 18, 2022

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.

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

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