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Machine Learning for Time-Series with Python: Forecast, predict, and detect anomalies with state-of-the-art machine learning methods
Machine learning has emerged as a powerful tool to understand hidden complexities in time-series datasets, which frequently need to be analyzed in areas as diverse as healthcare, economics, digital marketing, and social sciences. These datasets are essential for forecasting and predicting outcomes or for detecting anomalies to support informed decision making.
This book covers Python basics for time-series and builds your understanding of traditional autoregressive models as well as modern non-parametric models. 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.
Machine Learning for Time-Series with Python explains the theory behind several useful models and guides you in matching the right model to the right problem. The book also includes real-world case studies covering weather, traffic, biking, and stock market data.
By the end of this book, you will be proficient in effectively analyzing time-series datasets with machine learning principles.
This book covers Python basics for time-series and builds your understanding of traditional autoregressive models as well as modern non-parametric models. 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.
Machine Learning for Time-Series with Python explains the theory behind several useful models and guides you in matching the right model to the right problem. The book also includes real-world case studies covering weather, traffic, biking, and stock market data.
By the end of this book, you will be proficient in effectively analyzing time-series datasets with machine learning principles.
371 pages, Paperback
Published November 9, 2021
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Displaying 1 - 2 of 2 reviews
January 23, 2022
This book does a good job of capturing the state-of-the-art techniques for time series in every major area of Machine Learning into 300 pages.
Every chapter begins with a short introduction of the theory about the topic and methods. As every chapter covers a relatively broad field, do not expect exhaustive in-depth explanations for every algorithm but rather a quick overview of all the essentials you need to know to start. I genuinely enjoyed this approach as it gives enough information about the methods (which readers can explore further in original articles) while not being too long for someone already skilled in the topic.
Afterward, there is an outline of the most used python packages, which you can use to try out the theory from the chapter. It consists mainly of short code snippets, and I would compare it to assembled "Getting Started" sections from package documentation. I think it accomplishes the intended purpose of giving you a short example of how to use a particular package, but it does not go much further.
Even though prior knowledge about the topic is required, I would recommend this book to anyone looking for a starting point in machine learning for time series. One thing I was missing were more detailed comparisons of mentioned methods, their advantages, when and why to use them, but maybe that would require a book on its own.
Every chapter begins with a short introduction of the theory about the topic and methods. As every chapter covers a relatively broad field, do not expect exhaustive in-depth explanations for every algorithm but rather a quick overview of all the essentials you need to know to start. I genuinely enjoyed this approach as it gives enough information about the methods (which readers can explore further in original articles) while not being too long for someone already skilled in the topic.
Afterward, there is an outline of the most used python packages, which you can use to try out the theory from the chapter. It consists mainly of short code snippets, and I would compare it to assembled "Getting Started" sections from package documentation. I think it accomplishes the intended purpose of giving you a short example of how to use a particular package, but it does not go much further.
Even though prior knowledge about the topic is required, I would recommend this book to anyone looking for a starting point in machine learning for time series. One thing I was missing were more detailed comparisons of mentioned methods, their advantages, when and why to use them, but maybe that would require a book on its own.
August 5, 2025
While much of the non-coding material will remain relevant for many years, many of the coding portions of the book were non-functional less than a year after publication when I initially tried to get them to work. The author says he keeps his GitHub page up-to-date with the most recent code changes, but this is simply not true.
In the future, I'd recommend the author include environment requirement files to ensure environments are replicable for users several months after publication.
In the future, I'd recommend the author include environment requirement files to ensure environments are replicable for users several months after publication.
Displaying 1 - 2 of 2 reviews