Download Ebook Forecasting: principles and practice, by Rob J Hyndman

Download Ebook Forecasting: principles and practice, by Rob J Hyndman

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Forecasting: principles and practice, by Rob J Hyndman

Download Ebook Forecasting: principles and practice, by Rob J Hyndman

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Product details

Paperback: 382 pages

Publisher: OTexts; 2 edition (May 6, 2018)

Language: English

ISBN-10: 0987507117

ISBN-13: 978-0987507112

Product Dimensions:

6.7 x 0.9 x 9.6 inches

Shipping Weight: 1.8 pounds (View shipping rates and policies)

Average Customer Review:

4.0 out of 5 stars

19 customer reviews

Amazon Best Sellers Rank:

#184,814 in Books (See Top 100 in Books)

Authors have an engaging style and do a great job explaining several forecasting methods with applications as well as highlight the scope and differences among those. Earlier chapters provide a non-technical introduction to forecasting which would be appreciated by the people in the business world. Unlike many other books on times series analysis, this is not a heavily mathematical book. It is geared towards the education and somewhat painless implementation of the fundamental forecasting methods using R. As such, those seeking more advanced topics will only find a short final chapter around topics such as Dynamic Regression, VAR and Neural Nets. There is a website associated with this book which includes all the materials from the book, datasets, R codes, authors' and readers comments and much more. This is a great resource for those seeking additional help and those who may prefer reading the book's materials online, although reading the hard copy I found myself having to consult with this site a few times to find references to the R packages/functions and so on. On the downside, the book has no index, there are figures with no reference (e.g. Fig 2.3 and 2.4 have the same description and are not called in the text), and occasional typos are found here and there (I keep a note of these to hopefully communicate them to the authors). Overall, this is a great book and in authors' words "a comprehensive introduction to the latest forecasting methods using R".p.s. I am still working through different chapters so this review is subject to change.

While working on forecasting (understand “time series analysis”) I found several interesting and state of the art articles from Rob J. Hyndman. He is the co-author, with George Athanasopoulos of Forecasting: Principles and Practice. This is an excellent, concise and comprehensive text explaining concepts behind forecasting, common algorithms and how to implement them in R (for a business view of forecasting, I advise "Future Ready").The book presents key concepts of forecasting. From judgemental forecasting (which can be useful when you have no or few data) to simple/multiple regression, time series decomposition, exponential smoothing (ETS), ARIMA and a few more advanced topics such as Neural Networks. I would suggest to the author to add Support Vector Regression (SVR) and ensemble learning for the next edition of the book. Each concept of the book is covered through examples with real data. What is most appreciable about the book is how concise and readable it is. Each sentence is useful to understand the described concept, nothing superfluous.The book contains good overview and schema about each technique and how to set their meta-parameters. The R codes are well presented and easy to implement and test. The book can easily be used to teach forecasting since each chapter contains exercises. In conclusion, Forecasting: Principles and Practice is THE book to learn time series analysis algorithms and how to implement them in R.

This book is an excellent resource for anyone trying to master practical nuts and bolts of forecasting or who is just starting to study the field. The authors explain the practical issues needed to forecast. If you want to know about the distribution of the Durbin-Watson statistic, or other recondite details, this is not the right resource. The text is tightly integrated with R examples which make it easy to start applying immediately what you have learned. Note: I read the free web version before the text was released. An index, however, would have been helpful.

Money well-spent. RH knows his stuff, and is a teacher so ostensibly cares about whether his students/readers actually learn something. I'm a huge fan of his work with the R package "forecast" and his various other offerings. You'd be wise to turn your attention to him.

Great book on time-series forecasting!Very application oriented...

I love FPP, that's why I bought the paper version from Amazon. I was surprised that there was no index. Is there anyway you could generate an index for the printed version?

A good book for beginners in time series studies ...not many details though on various topics

Great goob, starting from simple to the complex. Good reference for the data scientist.

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