Forecasting: principles and practice Paperback – October 17, 2013

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.

The PAGES inside the book are printed only in Black and White. Therefore the charts/graphs are so hard to read. I was expecting a quality book for $45. Thats why i ended up giving 4 stars.

Excellent book with very broad coverage. Depth may be lacking some times and you may have to resort to the academic papers cited. There is no coverage of recent deep learning models like RNN and LSTM for forecasting.

This is a very good book for learning forecasting, with an emphasis on applying the ideas within the "R" environment.

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

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.

I am only half way through, but I can honestly say this is a very well written and thorough book. It is clear the writers are experts in their field. It does assume some maths knowledge (the book contains formulae), but the writers do explain it well so I would still say it is suitable for those less technical. It packs a lot in 300 pages.The examples are really good and not your standard, often too basic, examples - they use stock markets, beer production datasets etc which are more akin to the types of data you deal with if you have to do forecasting in a work context. They also highlight where people commonly make mistakes in forecasting and advise on how to avoid. To me, this is a sign of a GOOD textbook- many skirt around complex issues which means you have to hunt around on the internet/stackoverflow to get more detailed information.The only thing that was slightly annoying was the graphs are not printed in colour, however this can be cross referenced on their website. I'll be interested to see how more difficult topics are handled.

This is an immensely useful book. It is also very clearly written. Occasionally I had questions that it did not answer, but there's plenty of online guidance on using R from many places. All in all, a welcome overview of forecasting techniques.

Great book, even if it's for a very specific group of readers. It adds more information to what you can find in the otexts website.

I recommend this to engineers who need to know about forecasting. A very good introduction from the author of the R package 'forecast'.

Good product