Time Series Forecasting in Python

Great book that starts with very basic things and goes all the way to the very sophisticated concepts. It is very easy to follow, especially python code is clearly explained, almost line by line. After running examples of code for deep neural networks decided that I want to learn TensorFlow and ordered the next book from the same publisher.

It has covered the latest topics about applying machine learning in time series forecasting. It also briefly explains and illustrates many classical methods in Python. A very good book of introduction to learn time series forecast.

The jump to ML is done way too early

This is a very good book that takes the topics of time series for beginners to intermediate in a very accurate and explains things in very simple manner, this will help non technical folks get a good introduction to this topic. In summary very good book.

The structure of the book to get one started with concepts, thinking about the time series problem, and analysis makes this unique. Most of the books go after demonstrating the python code rather than trying to put the concepts together and using python examples to illustrate the concepts. The flow charts for decision making are nice and mathematical concepts are clearly explained. Great Book.

This book basically copy and pasted the Tensorflow tutorial on Time Series. You can read the Tensorflow tutorial for free and save money.

This book explains the concepts in a non-mathematical way. So, if you do not have a mathematics background then you should be able to follow along. Codes in the book sometimes do not work but if you look at the github page for the book, these errors have been corrected. My biggest problem with the book is that it does not show you how to forecast beyond your dataset. For example, if you have a monthly/weekly dataset ranging from 2010-2022, then this book does not show you how to forecast into the year 2023. All the models (at least the classic models that the book has talked about, I haven't looked at the deep learning ones yet) divide the dataset into training and test data and then show you how the model is performing against the test data. Now, that we know the models are performing well, how about forecasting beyond the test dataset? That part is missing in the book. So, in my opinion the book helps you to understand forecast theory in a non-mathematical way but does not actually show you how to forecast past the test data that you have available. Another negative is that the book repeats a lot of stuff. It seems like the author is justifying the high price of the book with the number of pages (at close to sixty dollars, this book is pretty expensive for what it is trying to teach). So, two stars for the theoretical part and took away three stars for not showing how to forecast past the test dataset. When someone buys a book on forecast, they normally hope to learn how to forecast into the future and not just learn how forecasting works within their available dataset. This book fails in this particular direction.

It’s a solid book, excellent introduction, and I definitely have a far greater understanding of the various models, and reasoning behind their components. I had hoped for a bit more, but I think the time series model family is just fairly simple in concept and practice — I should be thrilled it’s so simple, but yeah guess I wanted more of an intermediate read. I suppose I feel like I didn’t add much to my toolkit, more so just have a greater understanding of them — hoped for a bit of both. I also bought this pre release when index wasn’t fully formed, so had I read the full index I probably would have realized this pre buy — the fault lies with me there.

El autor explica todo muy bien. Empieza con ejemplos muy sencillos y los va complicando poco a poco. No llegas a perderte, porque el autor tiene la habilidad de ir paso a paso.

Excelente libro, muy claro, completo y actualizado.