Items related to Practical Time Series Forecasting: A Hands-on Guide
Synopsis
Practical Time Series Forecasting is a hands-on introduction to quantitative forecasting of time series. Quantitative forecasting is an important component of decision making in a wide range of areas and across many business functions including economic forecasting, workload projections, sales forecasts, and transportation demand. Of course, forecasting is widely used also outside of business, such as in demography and climatology.The book introduces readers to the most popular statistical models and data mining algorithms used in practice. It covers issues relating to different steps of the forecasting process, from goal definition through data visualization, modeling, performance evaluation to model deployment.Practical Time Series Forecasting is suitable for courses on forecasting at the upper-undergraduate and graduate levels. It offers clear explanations, examples, end-of-chapter problems and a case.Methods are illustrated using XLMiner, an Excel add-on. However, any software that has time series forecasting capabilities can be used with the book.Galit Shmueli is Professor of Statistics at the University of Maryland's Smith School of Business. She is co-author of the textbook Data Mining for Business Intelligence, and the book Modeling Online Auctions, among many other publications in professional journals. She has been teaching courses on forecasting and data mining at the University of Maryland, the Indian School of Business, and online at Statistics.com.
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About the Author
Galit Shmueli is Associate Professor of Statistics in the department of Decision, Operations & Information Technologies at the Smith School of Business, University of Maryland. She is co-director of the eMarkets research lab, a member of the Human-Computer Interaction Lab, and the Center for Health and Information Decision Systems. Dr. Shmueli's research focuses on statistical and data mining methods for modern data structures, with a focus on "statistical strategy" - issues related to how data analytics are used in scientific research. Her main field of application is information systems (in particular, electronic commerce). Dr. Shmueli's research has been published in the statistics, information systems, and marketing literature. She has authored over fifty journal articles and book chapters, has co-authored several books, and is on the editorial boards of several journals. She presents her work nationally and internationally. After receiving her PhD in Statistics from the Israel Institute of Technology in 2000, Dr. Shmueli was visiting faculty at Carnegie Mellon University's statistics department, where she became involved in the early research in biosurveillance. After moving to the University of Maryland, she initiated with Dr. Jank a new research field on the interface of statistics and information systems called "statistical methods in eCommerce". This now highly active interdisciplinary field has generated important advancements in empirical eCommerce research. Dr. Shmueli is passionate about teaching statistics and data mining and improving their application in the business environment. Her recent co-authored textbook "Data Mining for Business Intelligence" is part of her effort to create analytically-savvy MBAs.
Review
"The book is a little gem." - FORESIGHT, The International Journal of Applied Forecasting
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