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Publisher ‏ : ‎ BPB Publications (October 15, 2021)
Language ‏ : ‎ English
Paperback ‏ : ‎ 314 pages
ISBN-10 ‏ : ‎ 9391392571
ISBN-13 ‏ : ‎ 978-9391392574
Item Weight ‏ : ‎ 1.38 pounds
Dimensions ‏ : ‎ 7.5 x 0.71 x 9.25 inches

About the author

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Ivan Gridin

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Ivan Gridin is a machine learning expert. Over the years, he worked on distributive high-load systems and implemented different machine learning approaches in practice. One of the key areas of his research is the design and analysis of predictive time series models. Ivan has fundamental math skills in probability theory, random process theory, time series analysis, machine learning, deep learning, and optimization.

email: ivan.gridin.pro@gmail.com

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Tama

Original view and author feedback

Reviewed in the United States on January 10, 2022

Verified Purchase

Timeseries forecasting is a rather popular topic these days. A lot of experts are trying to apply deep learning techniques to create prediction models. This book contains an original view on this topic explaining the mechanics of timeseries pattern extraction.

After reading the book I could list the following:

Pros:
- Good robust code which can be used for other tasks
- Nice figures
- Latest architectures
- Hybrid models and very interesting topic of hyperparameter optimization

Cons:
- It doesn't cover any classical statistical models
- Author gives a very short introduction to ARIMA, HWES models
- I would add some deep learning architectures
- No machine learning techniques (like scikit-learn), only deep learning (PyTorch)

I've planned to leave 4 stars review, but I had a question about implementing the TCN model to my problem and I've emailed the author to the email mentioned on his amazon's author page. And Ivan Gridin responded the same day with a clear explanation and said that he will put this example in the 2nd edition of this book. And that was a positive experience for me, so I put 5 for this feedback.

I think this book can be a good starting point for diving into Timeseries Deep Learning.

8 people found this helpful

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Parker

Excellent explanation of deep learning principles and designs applied to forecasting!!!

Reviewed in the United States on October 31, 2021

The book I've been looking for! A very good fresh view of this topic!

There are no useless "hello world" examples that have no practical usage. Book describes only specific deep learning techniques which are aimed to predict timeseries.

Special thanks to the introduction to neural network architectures search and hybrid models. These new ideas helped me a lot.

Well done!

2 people found this helpful

Matthew

very informative

Reviewed in the United States on November 13, 2022

detailed and informative, must read for any TS associated work.
UPDATEd, critical typo errors.
CASUAL Convolution should be Causal Convolution

Kevin Stones

Great book combining deep learning and forecasting problems

Reviewed in the United States on November 17, 2021

Nice and gentle explanation of the latest advances in time series forecasting using deep learning methods. After this book, I've really understood how recurrent networks work! Also, an amazing explanation of Temporal Convolutional Networks. I've liked the stock market prediction model the most!

Amazon Customer

Clear and to the point

Reviewed in the United States on March 29, 2022

Somewhat rarely in DR - code works out of the box with a normal setup, descriptions are clear and concise - recommended!

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Salvatore

Muy buen libro

Reviewed in Mexico on April 11, 2022

Claro y preciso, sin complicaciones matemáticas. Ilustra muy bien los conceptos clave. Considero que su didáctica es muy adecuada.
Felicitaciones al autor.

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Deep

Great Book!

Reviewed in India on November 23, 2023

This books goes deep inside deep learning
Lots of useful and real world examples with great model designs!
A must read for time series analysis

Very Good with Clean Practical Code

Reviewed in the United Kingdom on February 19, 2023

Very good concise starting point for time series. It’s light on theory but the author makes this very clear in the introduction. This is a non-nonsense practical book, and assumes competence in python. The writing style is a bit terse in places but understandably so (given the author’s background), even so - the text is direct and clear. Liked that the author goes back to basics in code so that concepts are not just assumed or obscured by package calls (e.g., tensors, trend removal, alternate models). The more involved model descriptions (e.g., RNN/LSTN/TCN), can be a quite brief, you need to read between the lines, look at other sources, and work through the code.

There is less coverage on more advanced topics, confidence intervals, regular and irregular timestamps, complex correlations, optimisations. The author could have cited deeper material and summarised gaps between where this code stops and real-world solutions start, That said, this is a solid starting point for time series. Another 100 pages would have really pushed this up a notch and been equally enjoyable to read. Nice one.

CorMag

Good starting point....

Reviewed in Germany on February 4, 2022

I do work with time series data and deep learning models in practice.
Don't expect a super in depth treatment of the subject. That is not what the book tries to do

What would I suggest is missing for beginners? I think even for beginners a word on uncertainty estimates would have been very important. Point predictions are borderline worthless for real world applications.. The part on financial data is not great. This would have been a great way to show ways how to "break" your own model and do more tests. I didn't look deeper into it, but I am prtty sure the model has no predictive power.

But the hack of applying a fiter (Christano Fitzgerald , Baxter King..) to raw input, CNNs for time series and the emphasis of hybrid models show the right spririt: presenting things that can be really valuable in practice.

The reader needs to be familiar with python. But the source code is not "production" code, but almost like pseudo code. Easy to read and good for explaining the basic ideas.

Like the authors other book on genetic algorithms: for the money paid, it is a really good and hands on starter on the topic. There are many time series deep learning framworks around (GluonTS, PYtorch forecasting,....)

Julian

Concise and useful

Reviewed in Japan on January 5, 2023

Concise explanations of time series networks and useful examples to know how we can implement the theories

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Time Series Forecasting using Deep Learning: Combining PyTorch, RNN, TCN, and Deep Neural Network Models to Provide Production-Ready Prediction Solutions (English Edition) Paperback – October 15, 2021

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