News

Book Adopted at TAPMI

posted Jan 24, 2017, 7:18 AM by Boaz Shmueli

Warm welcome to Prof. Kartikeya Bolar and his students who will be using our course in an upcoming Forecasting Models course at T.A. Pai Management Institute (TAPMI), an AACSB-accredited institute.

There are a total of 5 AACSB-accredited institutions in India: In addition to TAMPI, they are ISB, XLRI, IIM-C, and IMT. Our book is now used at both ISB and TAMPI, covering 40% of India :-)

Hardcovers now available!

posted Jan 17, 2017, 5:57 AM by Boaz Shmueli   [ updated Jan 17, 2017, 7:04 PM ]

We are excited to announce that the Practical Time Series Forecasting textbooks are now available also in beautiful hardcover (in addition to softcover and Kindle). 
The hardcovers are only $49.90 on Amazon (R edition or XLMiner edition), or can be ordered from any local bookstore. 
Get your copies today!

Book adopted at Concordia University

posted Jan 17, 2017, 5:38 AM by Boaz Shmueli

Warm welcome to Prof. Dennis Kira of John Molson School of Business, Concordia University, who is using our book with the course Business Forecasting (BSTA 477/DESC 677)

Announcing More Adoptions

posted Jan 4, 2017, 5:27 AM by Boaz Shmueli   [ updated Jan 4, 2017, 7:13 PM ]

A warm welcome to the following professors and their students who have recently adopted our book for their courses:
  • Prof. Don Lien, University of Texas at San Antonio
  • Prof. Prodan-Boul, University of Houston
  • Prof. Inga Maslova, USC Marshall School of Business
Happy learning and a happy new year!

More adoptions - ISB and Rockhurst U!

posted Dec 8, 2016, 12:15 AM by Boaz Shmueli

Warm welcome to Prof. Lai and his MPMO students at the Indian School of Business who will be using "Practical Time Series Forecasting with R" in the course Supply Chain Analytics!

And extending a warm welcome also to Prof. O'Connor and his students at the Helzberg School of Management at Rockhurst University who will be using "Practical Time Series Forecasting with R" in the course Forecasting and Data Analysis for Decision Making.

Supporting Videos Now Available

posted Dec 2, 2016, 1:17 AM by Boaz Shmueli   [ updated Dec 2, 2016, 1:21 AM ]

YouTube playlistA series of 39 videos by award-winning instructor Galit Shmueli are now available to support the book. 
The videos average at around 8 minutes each, and the total playing time is over 5 hours.

New adoption at Brandeis University

posted Nov 21, 2016, 6:48 AM by Boaz Shmueli

Warm welcome to Prof. Blake LeBaron and the students at Brandeis International Business School who will be using our textbook Practical Time Series Forecasting with R in the course Forecasting in Finance & Economics!

New R Edition at USC Marshall

posted Oct 22, 2016, 1:57 AM by Boaz Shmueli

Happy learning to Profs. Gabrys & Abbass course "Applied Time Series Analysis For Forecasting" at USC Marshall Masters of Science in Business Analytics using the new R version of Practical Time Series Forecasting!

Book Adopted at Amrita University

posted Sep 21, 2016, 2:13 AM by Boaz Shmueli

Amrita UniversityHappy learning to Professor Vivek Menon at Amrita University's MBA Program!

New Edition of Practical Time Series Forecasting with R

posted Jul 26, 2016, 9:06 AM by Boaz Shmueli   [ updated Jul 27, 2016, 11:22 PM ]

Practical Time Series Forecasting with R, 3rd Edition
Practical Time Series Forecasting with R, Second Edition is now in print (a Kindle version is also available; an Indian Edition is coming soon). Here is a summary of the changes:

Based on feedback from readers and instructors, the Second Edition has two main improvements. First is a new-and-improved structuring of the topics. This reordering of topics is aimed at providing an easier introduction of forecasting methods which appears to be more intuitive to students. It also helps prioritize topics to be covered in a shorter course, allowing optional coverage of topics in Chapters 8-9. The restructuring also aligns this new edition with the new Third Edition of XLMiner®-based Practical Time Series Forecasting, offering instructors the flexibility to teach a mixed crowd of programmers and non-programmers. The re-ordering includes
  • relocating and combining the sections on autocorrelation, AR and ARIMA models, and external information into a separate new chapter (Chapter 7). The discussion of ARIMA models now includes equations and further details on parameters and structure
  • forecasting binary outcomes is now a separate chapter (Chapter 8), introducing the context of binary outcomes, performance evaluation, and logistic regression
  • neural networks are now in a separate chapter (Chapter 9)
The second update is the addition and expansion of several topics:
  • prediction intervals are now included on all relevant charts and a discussion of prediction cones was added
  • The discussion of exponential smoothing with multiple seasonal cycles in Chapter 5 has been extended, with examples using R functions dshw and tbats
  • Chapter 7 includes two new examples (bike sharing rentals and Walmart sales) using R functions tslm and stlm to illustrate incorporating external information into a linear model and ARIMA model. 
Additionally, the STL approach for decomposing a time series is introduced and illustrated.

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