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Ivan Gridin
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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) Kindle Edition

by Ivan Gridin (Author) Format: Kindle Edition
4.3 4.3 out of 5 stars 48 ratings
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Explore the infinite possibilities offered by Artificial Intelligence and Neural Networks

Key Features
● Covers numerous concepts, techniques, best practices and troubleshooting tips by community experts.
● Includes practical demonstration of robust deep learning prediction models with exciting use-cases.
● Covers the use of the most powerful research toolkit such as Python, PyTorch, and Neural Network Intelligence.

Description
This book aims to teach the readers how to apply deep learning techniques to the time series forecasting challenges and how to build prediction models using PyTorch.
The readers will learn the fundamentals of PyTorch in the early stages of the book. Next, the time series forecasting is covered in greater depth after the program has been developed. You will try to use machine learning to identify the patterns that can help us forecast future results. It covers methodologies such as Recurrent Neural Network, Encoder-decoder model, and Temporal Convolutional Network, all of which are state-of-the-art neural network architectures. Furthermore, for good measure, we have also introduced the neural architecture search, which automates searching for an ideal neural network design for a certain task.
Finally, by the end of the book, readers would be able to solve complex real-world prediction issues by applying the models and strategies learned throughout the book. This book also offers another great way of mastering deep learning and its various techniques.


What you will learn
● Work with the Encoder-Decoder concept and Temporal Convolutional Network mechanics.
● Learn the basics of neural architecture search with Neural Network Intelligence.
● Combine standard statistical analysis methods with deep learning approaches.
● Automate the search for optimal predictive architecture.
● Design your custom neural network architecture for specific tasks.
● Apply predictive models to real-world problems of forecasting stock quotes, weather, and natural processes.

Who this book is for
This book is written for engineers, data scientists, and stock traders who want to build time series forecasting programs using deep learning. Possessing some familiarity of Python is sufficient, while a basic understanding of machine learning is desirable but not needed.

Table of Contents
1. Time Series Problems and Challenges
2. Deep Learning with PyTorch
3. Time Series as Deep Learning Problem
4. Recurrent Neural Networks
5. Advanced Forecasting Models
6. PyTorch Model Tuning with Neural Network Intelligence
7. Applying Deep Learning to Real-world Forecasting Problems
8. PyTorch Forecasting Package
9. What is Next?
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  1. Print length
    421 pages
  2. Language
    English
  3. Publisher
    BPB Publications
  4. Publication date
    October 15, 2021
  5. File size
    4877 KB
  6. Page Flip
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  7. Word Wise
    Not Enabled
  8. Enhanced typesetting
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Product details

  • ASIN ‏ : ‎ B09JL2B3YX
  • Publisher ‏ : ‎ BPB Publications; 1st edition (October 15, 2021)
  • Publication date ‏ : ‎ October 15, 2021
  • Language ‏ : ‎ English
  • File size ‏ : ‎ 4877 KB
  • Text-to-Speech ‏ : ‎ Enabled
  • Screen Reader ‏ : ‎ Supported
  • Enhanced typesetting ‏ : ‎ Enabled
  • X-Ray ‏ : ‎ Not Enabled
  • Word Wise ‏ : ‎ Not Enabled
  • Print length ‏ : ‎ 421 pages
  • Customer Reviews:
    4.3 4.3 out of 5 stars 48 ratings

About the author

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Ivan Gridin

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

Customer reviews

4.3 out of 5 stars
48 global ratings
Excellent explanation of deep learning principles and designs applied to forecasting!!!
5 out of 5 stars
Excellent explanation of deep learning principles and designs applied to forecasting!!!
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!
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Top reviews from the United States

SALID WORAWIBOOL
5.0 out of 5 stars Recommended book for time series forecasting with Python
Reviewed in the United States on October 30, 2024
Verified Purchase
Actually I focus on computer vision by using deep learning but I just expand my experience on time series forecasting. Thanks for your great work for sharing your knowledge and your thought into your book. I like your style of writing, teaching for design thinking, not too much explanation but clear enough, and useful codes for playing with dataset and for implementation.
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Tama
5.0 out of 5 stars 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
5.0 out of 5 stars Excellent explanation of deep learning principles and designs applied to forecasting!!!
Reviewed in the United States on October 31, 2021
Verified Purchase
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!
Customer image
Parker
5.0 out of 5 stars 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!
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2 people found this helpful
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Matthew
5.0 out of 5 stars very informative
Reviewed in the United States on November 13, 2022
Verified Purchase
detailed and informative, must read for any TS associated work.
UPDATEd, critical typo errors.
CASUAL Convolution should be Causal Convolution
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Kevin Stones
5.0 out of 5 stars Great book combining deep learning and forecasting problems
Reviewed in the United States on November 17, 2021
Verified Purchase
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!
2 people found this helpful
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Amazon Customer
5.0 out of 5 stars Clear and to the point
Reviewed in the United States on March 29, 2022
Verified Purchase
Somewhat rarely in DR - code works out of the box with a normal setup, descriptions are clear and concise - recommended!
2 people found this helpful
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Top reviews from other countries

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Salvatore
5.0 out of 5 stars Muy buen libro
Reviewed in Mexico on April 11, 2022
Verified Purchase
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
5.0 out of 5 stars Great Book!
Reviewed in India on November 23, 2023
Verified Purchase
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
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ak
4.0 out of 5 stars Very Good with Clean Practical Code
Reviewed in the United Kingdom on February 19, 2023
Verified Purchase
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.
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CorMag
4.0 out of 5 stars Good starting point....
Reviewed in Germany on February 4, 2022
Verified Purchase
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,....)
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Julian
5.0 out of 5 stars Concise and useful
Reviewed in Japan on January 5, 2023
Verified Purchase
Concise explanations of time series networks and useful examples to know how we can implement the theories
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