Forecasting: principles and practice

Free sample

Forecasting is required in many situations. Stocking an inventory may require forecasts of demand months in advance. 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.

Read more

About the author

Rob J Hyndman is Professor of Statistics at Monash University, Australia. He is Editor-in-Chief of the International Journal of Forecasting, Editor of the Journal of Statistical Software and a Director of the International Institute of Forecasters. He is author of over 100 research papers in statistical science. In 2007, he received the Moran medal from the Australian Academy of Science for his contributions to statistical research. For over 25 years, Rob has maintained an active consulting practice, assisting hundreds of companies and organizations on forecasting problems.

George Athana­sopou­los is an Associate Professor in the Department of Econometrics and Business Statistics at Monash University, Australia. He received a PhD in Econometrics from Monash University in 2007, and has received many awards and distinctions for his research. His research interests include multivariate time series analysis, forecasting, non-linear time series, wealth and tourism economics. He is on the Editorial Boards of the Journal of Travel Research and the International Journal of Forecasting. 

Read more
34 total

Additional Information

Read more
Published on
Sep 20, 2014
Read more
Read more
Read more
Read more
Best For
Read more
Read more
Business & Economics / Forecasting
Business & Economics / Statistics
Computers / Databases / Data Mining
Computers / Mathematical & Statistical Software
Language Arts & Disciplines / Library & Information Science / General
Social Science / Research
Read more
Content Protection
This content is DRM protected.
Read more

Reading information

Smartphones and Tablets

Install the Google Play Books app for Android and iPad/iPhone. It syncs automatically with your account and allows you to read online or offline wherever you are.

Laptops and Computers

You can read books purchased on Google Play using your computer's web browser.

eReaders and other devices

To read on e-ink devices like the Sony eReader or Barnes & Noble Nook, you'll need to download a file and transfer it to your device. Please follow the detailed Help center instructions to transfer the files to supported eReaders.
Master Bayesian Inference through Practical Examples and Computation–Without Advanced Mathematical Analysis

Bayesian methods of inference are deeply natural and extremely powerful. However, most discussions of Bayesian inference rely on intensely complex mathematical analyses and artificial examples, making it inaccessible to anyone without a strong mathematical background. Now, though, Cameron Davidson-Pilon introduces Bayesian inference from a computational perspective, bridging theory to practice–freeing you to get results using computing power.

Bayesian Methods for Hackers illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib. Using this approach, you can reach effective solutions in small increments, without extensive mathematical intervention.

Davidson-Pilon begins by introducing the concepts underlying Bayesian inference, comparing it with other techniques and guiding you through building and training your first Bayesian model. Next, he introduces PyMC through a series of detailed examples and intuitive explanations that have been refined after extensive user feedback. You’ll learn how to use the Markov Chain Monte Carlo algorithm, choose appropriate sample sizes and priors, work with loss functions, and apply Bayesian inference in domains ranging from finance to marketing. Once you’ve mastered these techniques, you’ll constantly turn to this guide for the working PyMC code you need to jumpstart future projects.

Coverage includes

• Learning the Bayesian “state of mind” and its practical implications

• Understanding how computers perform Bayesian inference

• Using the PyMC Python library to program Bayesian analyses

• Building and debugging models with PyMC

• Testing your model’s “goodness of fit”

• Opening the “black box” of the Markov Chain Monte Carlo algorithm to see how and why it works

• Leveraging the power of the “Law of Large Numbers”

• Mastering key concepts, such as clustering, convergence, autocorrelation, and thinning

• Using loss functions to measure an estimate’s weaknesses based on your goals and desired outcomes

• Selecting appropriate priors and understanding how their influence changes with dataset size

• Overcoming the “exploration versus exploitation” dilemma: deciding when “pretty good” is good enough

• Using Bayesian inference to improve A/B testing

• Solving data science problems when only small amounts of data are available

Cameron Davidson-Pilon has worked in many areas of applied mathematics, from the evolutionary dynamics of genes and diseases to stochastic modeling of financial prices. His contributions to the open source community include lifelines, an implementation of survival analysis in Python. Educated at the University of Waterloo and at the Independent University of Moscow, he currently works with the online commerce leader Shopify.

Ord/Fildes PRINCIPLES OF BUSINESS FORECASTING, 1E serves as both a textbook for students and as a reference book for experienced forecasters in a variety of fields. The authors' motivation for writing this book is to give users the tools and insight to make the most effective forecasts drawing on the latest research ideas. Ord and Fildes have designed PRINCIPLES OF BUSINESS FORECASTING for users who have taken a first course in applied statistics or who have an equivalent background. This book introduces both standard and advanced forecasting methods and their underlying models, and also includes general principles to guide and simplify forecasting practice. A key strength of the book is its emphasis on real data sets, taken from government and business sources and used in each chapter's examples. Forecasting techniques are demonstrated using a variety of software platforms and the companion website provides easy-to-use Excel macros to support the basic methods. After the introductory chapters, the focus shifts to using extrapolative methods (exponential smoothing and ARIMA) and then to statistical model-building using multiple regression. The authors also cover more novel techniques including data mining and judgmental methods, which are gaining increasing attention in applications. Finally, they examine organizational issues of implementation and the development of a forecasting support system within an organization.
Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.
©2018 GoogleSite Terms of ServicePrivacyDevelopersArtistsAbout Google
By purchasing this item, you are transacting with Google Payments and agreeing to the Google Payments Terms of Service and Privacy Notice.