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Forecasting: principles and practice 2nd Edition

by Rob J Hyndman (Author), George Athanasopoulos (Author)
4.5 4.5 out of 5 stars 166 ratings
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Forecasting: Principles and Practice
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Forecasting is required in many situations. Deciding whether to build another power generation plant in the next five years requires forecasts of future demand. Scheduling staff in a call centre next week requires forecasts of call volumes. Stocking an inventory requires forecasts of stock requirements. Telecommunication routing requires traffic forecasts a few minutes ahead. Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning.This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly. Examples use R with many data sets taken from the authors' own consulting experience.In this second edition, all chapters have been updated to cover the latest research and forecasting methods. Three new chapters have been added on dynamic regression forecasting, hierarchical forecasting and practical forecasting issues. The latest version of the book is freely available online at http://OTexts.com/fpp2.
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  1. ISBN-10
    0987507117
  2. ISBN-13
    978-0987507112
  3. Edition
    2nd
  4. Publication date
    May 6, 2018
  5. Language
    English
  6. Dimensions
    6.69 x 0.9 x 9.61 inches
  7. Print length
    382 pages
  8. See all details
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Product details

  • Publisher ‏ : ‎ OTexts; 2nd edition (May 6, 2018)
  • Language ‏ : ‎ English
  • Paperback ‏ : ‎ 382 pages
  • ISBN-10 ‏ : ‎ 0987507117
  • ISBN-13 ‏ : ‎ 978-0987507112
  • Item Weight ‏ : ‎ 1.76 pounds
  • Dimensions ‏ : ‎ 6.69 x 0.9 x 9.61 inches
  • Customer Reviews:
    4.5 4.5 out of 5 stars 166 ratings

About the author

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Rob J Hyndman

Rob J Hyndman

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Professor Rob J Hyndman FAA FASSA (1967-) is an Australian statistician based at Monash University, Melbourne, Australia. He is best-known for his work in statistical forecasting, and was Editor-in-Chief of the International Journal of Forecasting from 2005-2018. In 2007, he won the Moran medal from the Australian Academy of Science for his contributions to research in statistics. In 2021, he won the Pitman medal from the Statistical Society of Australia. He is an elected Fellow of the Australian Academy of Science and the Academy of the Social Sciences in Australia. Further information is available on his website: robjhyndman.com

Customer reviews

4.5 out of 5 stars
166 global ratings

Customers say

Customers find the book useful for beginners to advanced readers. They say it has excellent examples and interesting data sets. Readers also mention the book is good for reference, with interesting software.

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ReadabilityContent quality
14 customers mention "Readability"12 positive2 negative

Customers find the book useful for both beginners and advanced readers. They say it explains the basics and provides an overview of time series forecasting. Readers also mention the book has useful code.

"...You will need to have some experience in R. The book is useful for both people versatile in math /statistic and people who are not statisticians...." Read more

"It is easy to read and has excellent examples. I like how R code is integrated throughout the text (the authors developed some of the R libraries)...." Read more

"...Many interesting data sets• Useful software, with several valuable R-functions" Read more

"a great book to understand the topic off forecasting and also to use R" Read more

3 customers mention "Content quality"3 positive0 negative

Customers find the book easy to read and has excellent examples. They appreciate the interesting data sets and useful software.

"It is easy to read and has excellent examples. I like how R code is integrated throughout the text (the authors developed some of the R libraries)...." Read more

"...There are a several good aspects to this book• Many interesting data sets• Useful software, with several valuable R-functions" Read more

"...Tons of examples, R code...." Read more

Top reviews from the United States

Nino
5.0 out of 5 stars A MUST have for practitioners
Reviewed in the United States on January 12, 2021
Verified Purchase
I bought the Kindle book but I actually read the otext.com online version where you can see the graphs and the equations much better. My review is about the content of the book, not about the format.
This is a great book. It reads like a hands-on manual how to use many different forecasting techniques in R. There is a lot of useful code. It does not dive deep into the math behind the techniques but for people who want to do this, the authors offer references in the "Further reading" sections. The book covers pretty much all major forecasting methods: regressions, exponential smoothing, ARIMA, dynamic regressions (ARIMA plus independent variables), and some advanced techniques such as VAR, bagging, neural networks.
The book is for anybody who is interested in learning how to forecast: from experienced data scientists to beginners. You will need to have some experience in R. The book is useful for both people versatile in math /statistic and people who are not statisticians. This is the right book for practitioners who need to forecast.
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EM
5.0 out of 5 stars I wish I would have had this book as a student!
Reviewed in the United States on June 30, 2020
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It is easy to read and has excellent examples. I like how R code is integrated throughout the text (the authors developed some of the R libraries). It surveys the important forecasting and time series models with enough rigor to understand how the methods work, without getting into so many details to lose the flow. I also appreciate having exercises at the end of every chapter. I will be using this as a textbook for my business masters students.
One person found this helpful
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anonymous
4.0 out of 5 stars I have used this book in a graduate Business Analytics program at a US Business College
Reviewed in the United States on October 27, 2020
Verified Purchase
In general, a good book. Attached a few comments to make it even better:

The sequencing of topics could be improved

• Chapter 4 on Judgmental Forecasting is out of place
• The regression chapter (Chapter 5) should come after the chapters on time series forecasting
• There is too much material on time series decompositions (Chapter 6)

The first part on exponential smoothing in Chapter 7 is good. However,

• The material on innovation state space models should be reconsidered. The book gives only selected references to the vast literature on Kalman Filter state space models. References to other work should be included.
• Prediction intervals for h > 1 could be handled better. It would be much better to use the standard deviation of historic h-step-ahead forecast errors (the R command tsCV is useful; one can do so with just a few lines of R code)
• Incorporating damping is dangerous, and cautionary remarks should be included. One cannot get reliable estimates on the damping parameter if parameters are estimated by minimizing the sum of squared one-step-ahead forecast errors
• Estimating starting values is ok, but simple initialization is good enough

Chapter 8 on time series modeling could be improved

• Highly unlikely that an average reader is able to understand how to build an appropriate ARIMA model
• More time should be spent on the iterative nature of model building
• There is little discussion of the importance of parsimony
• No discussion why it is important to check for trend components if the model contains only a seasonal difference, but no regular difference

The automatic model fitting strategy for both the time series models in Chapter 8 and the regression models with time series errors in Chapter 9 is prone to overfitting the time series models by adding terms to both the autoregressive and the moving average part of the model. An ARIMA(p,d,q) model has many equivalent representations that are obtained when one adds the same factor to both the AR and the MA operator and considers non-parsimonious models of the from ARIMA(p+1,d,q+1), ARIMA(p+2,d,q+2), and so on. The automatic ARIMA models tend to include AR and MA components of large order that include almost identical factors.

There is too much emphasis on the R-code. More time should be spent on the topic of forecasting. Piping R instructions are confusing for students. It is preferable to write out separate (step-by-step) R code. While it is not as elegant, it would not confuse the beginning student.

There are a several good aspects to this book

• Many interesting data sets
• Useful software, with several valuable R-functions
12 people found this helpful
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Sylvie Ratté
5.0 out of 5 stars The perfect book to understand temporal series
Reviewed in the United States on August 10, 2020
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I truly appreciate the hands-on approach put forward by the authors. Tons of examples, R code. All is there to replicate the results and acquire a better understanding of forecasting and temporal series mining in particular. I used it in one of the module for my data mining class for Master and Ph.D. students. A must-have.
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Jose Francisco Valverde Calderon
5.0 out of 5 stars useful book
Reviewed in the United States on June 18, 2021
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a great book to understand the topic off forecasting and also to use R
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Venky
5.0 out of 5 stars Nice Product
Reviewed in the United States on April 13, 2021
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It came at the right time, and the product was good.
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Kevin in Texas
5.0 out of 5 stars Great book on time-series forecasting!
Reviewed in the United States on November 29, 2018
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Great book on time-series forecasting!
Very application oriented...
One person found this helpful
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Scott D. Duncan
4.0 out of 5 stars Good up-to-date coverage of a complex and quickly evolving topic
Reviewed in the United States on April 27, 2021
Verified Purchase
Easy to read for anyone with some solid math skills (series, matrices, etc.) Does a good job of introducing and providing an overview of Time Series Forecasting (which is most forecasting.) My only complaint is that some important topics (like detecting outliers) are not covered at all, and even a couple of pages on these more advanced subjects (which are clearly labeled in the text as 'out of scope') would be appropriate & desirable, to at least introduce the topics & why they are important. Also, for me personally, I will have to 'translate' the techniques into Python to use them, but that is not a knock against the book as the authors clearly stated that R is the language of all of the examples, and that is probably appropriate for an intro / broad survey book like this, as R is simpler to learn than Python.
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veronica alcaraz
5.0 out of 5 stars maravilloso
Reviewed in Mexico on January 18, 2021
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Justo lo que requiero para mi maestria
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Brigitta
5.0 out of 5 stars beste Einführung in Zeitreihenanalyse
Reviewed in Germany on August 26, 2022
Verified Purchase
Das Buch ist online frei verfügbar. Ich habe es trotzdem noch als ebook gekauft - aus purer Dankbarkeit. Ohne dieses Buch hätte ich mein Seminar in Statistik nicht geschafft.
Eine wunderbare Einführung in Zeitreihenanalyse mit Beispielen in R.
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Ana Soares
5.0 out of 5 stars Mão na massa
Reviewed in Brazil on October 15, 2020
Verified Purchase
O livro vem com muitos esclarecimentos práticos de como lidar com o assunto. Gostei muito porque é escrito, de fato, para o R e com esclarecimentos sobre as técnicas utilizadas.
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Gaurav Pant
1.0 out of 5 stars Unable to download the book to my Kindle white
Reviewed in India on March 31, 2021
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The media could not be loaded.
I am unable to download the book to my Kindle white. How am I supposed to read it?
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Salvador Fandino
5.0 out of 5 stars Very good introduction to time series using R
Reviewed in Spain on December 20, 2020
Verified Purchase
The book is very good as a broad introduction to the subject.

Note that an updated version 3 is available on the book website.
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