We suggest these sites to the forecasting community. However, the IIF does not control nor take a position on the views they express. To add a resource to our site, contact [email protected] and reference ‘resources.’
The Conference Board This site provides economic and business cycle indicators.
Demand Forecasting Provides global training for corporate demand forecasters/planners/managers.
FAIRMODEL Ray Fair enables you to interact online with U.S. and multicountry econometric models.
Forecasting Principles Resource for finding forecasting related data, research, links, and other information. Created in 1997 by Professor J. Scott Armstrong, Wharton School, University of Pennsylvania, it is managed by Scott Armstrong and Kesten C. Green.
George Washington University The Research Program on Forecasting supports research, teaching, and dissertation supervision in forecasting as part of the Department of Economics and the Center for Economic Research at The George Washington University.
INFORMS The Institute for Operations Research and the Management Sciences (INFORMS) is the largest professional society in the world for professionals in the field of operations research (O.R.), management science, and business analytics.
Centre for Marketing Analytics and Forecasting A non-profit research-driven organisation, they provide unbiased services to the corporate and public sector – independent of methods, techniques, software products and vendors. The Centre offers 15 years of expertise in over 20 corporate projects per year on the complete range of predictive analytics, from demand planning to market modelling, from statistical methods to artificial intelligence and from public sector to corporations in telecommunications, fast moving consumer goods, insurances and manufacturing.
The Portal on Forecasting with Neural Networks
SAS Forecasting and Econometrics Community A first line of support for both SAS software and more general questions in forecasting and econometric analysis. When you are stuck on a problem just post a question, and the community of SAS experts will share their knowledge. This site also includes weekly articles and hot tips, as well as links to blog posts, upcoming events, and other sources of information.
Statistical Forecasting Fuqua School of Business, Duke University. Contains notes and materials for an advanced MBA elective course, with an emphasis on regression and ARIMA methods. Regression and descriptive data analysis are illustrated with, RegressIt, a recently-released free Excel add-in.
Sign up for the IIF blog! If interested in submitting a guest blog, contact us.
Book reviews for the International Journal of Forecasting: The IJF maintains a list of books which are available for review. If you are interested, please visit the website of Nikos Kourentzes. Here you will find a list of forecasting related books which are of high priority for review.
Armstrong, J. Scott ed., Principles of Forecasting, 2001. This summary of knowledge from experts and empirical studies provides guidelines that apply to economics, sociology, psychology and other fields. It addresses problems related to finance (How much is this company worth?), marketing (Will a new product be successful?), personnel (How can we identify the best job candidates?), and production (What level of inventories should be kept?). Edited by Professor J. Scott Armstrong of the Wharton School, University of Pennsylvania, this book contains contributions by 40 leading experts in forecasting. The 30 chapters cover all types of forecasting methods: judgmental methods, such as Delphi, role-playing, and intentions studies and quantitative methods, including econometric methods, expert systems, and extrapolation. Some methods, such as conjoint analysis, analogies, and rule-based forecasting, integrate quantitative and judgmental procedures. In each area, the authors identify what is known in the form of “if-then principles” and summarize evidence on these principles. The book also includes the first comprehensive forecasting dictionary.
Armstrong, J. Scott, Long-Range Forecasting: From Crystal Ball to Computer, John Wiley and Sons, 1985 (second edition). Designed to answer the question “Which forecasting method is the best to use in a given situation?” this book covers judgmental, extrapolation, and econometric methods. It shows how to combine forecasts and describes how to evaluate and compare different forecasting methods. The conclusions are based on the author’s research as well as on over 1050 books and papers on forecasting in economics, sociology, medicine, politics, weather, finance, personnel, marketing, and other areas. The book describes this vast array of literature. This second edition is out of print but is available online in a pdf of the full text.
Boylan, John and Syntetos, Aris, Intermittent Demand Forecasting: Context, Methods and Applications, Wiley, 2021. Intermittent Demand Forecasting is for anyone who is interested in improving forecasts of intermittent demand products, and enhancing the management of inventories. Whether you are a practitioner, at the sharp end of demand planning, a software designer, a student, an academic teaching operational research or operations management courses, or a researcher in this field, we hope that the book will inspire you to rethink demand forecasting. If you do so, then you can contribute towards significant economic and environmental benefits. Purchase at the link above and receive 20% discount; enter code IDF20.
Fildes, Robert and Ord, Keith, Principles of Business Forecasting, Cengage, 2012. This book serves both as a textbook for students and as a reference book for experienced forecasters in the many fields where forecasting is applied. The book introduces both standard and advanced forecasting methods and their underlying models, and also includes general principles to guide and simplify forecasting practice. The overall aim remains one of ensuring the techniques can be applied. 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 and exercises. 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.
Ferrara, Laurent, Hernando, Ignacio, Marconi, Daniela (eds.), International Macroeconomics in the Wake of the Global Financial Crisis, Springer, 2018. This book collects selected articles addressing several currently debated issues in the field of international macroeconomics. They focus on the role of the central banks in the debate on how to come to terms with the long-term decline in productivity growth, insufficient aggregate demand, high economic uncertainty and growing inequalities following the global financial crisis.
Gilliland, M., Tashman, L., and Sglavo, U. eds., Business Forecasting: Practical Problems and Solutions, Wiley, 2015. A compilation of some of the field’s important and influential literature into a single, comprehensive reference for forecast modeling and process improvement. Packed with provocative ideas from forecasting researchers and practitioners, on topics including accuracy metrics, benchmarking, modeling of problem data, and overcoming dysfunctional behaviors. Its coverage includes often-overlooked issues at the forefront of research, such as uncertainty, randomness, and forecastability, as well as emerging areas like data mining for forecasting. The articles present critical analysis of current practices and consideration of new ideas. With a mix of formal, rigorous pieces and brief introductory chapters, the book provides practitioners with a comprehensive examination of the current state of the business forecasting field.
Gilliland, Michael, The Business Forecasting Deal, Wiley, 2010. This book provides practical solutions to a wide variety of business forecasting problems. It illustrates how to think about business forecasting in the context of uncertainty, randomness, and process performance, all within the internal political arena in which real-life forecasting is conducted. Instead of focusing solely on statistical modeling and forecast accuracy (accuracy being largely determined by the nature of what is being forecast), this book focuses on forecasting process efficiency and effectiveness. It shows how to use Forecast Value Added (FVA) analysis to identify waste and inefficiency in a typical forecasting process, and how to eliminate the “worst practices” that sabotage an organization’s forecasting efforts.
González-Rivera, Gloria, Forecasting for Economics and Business, Prentice Hall, 2013. The general aim of this textbook is to develop sophisticated professionals able to critically analyze time series data and to produce sound forecasting reports. It provides a modern hands-on approach to the practice of forecasting. It covers forecasting (point forecasts and density forecasts) based on univariate and multivariate models for either stationary or non-stationary data, forecasting of volatility with interesting financial applications, and introductory material to forecasting with non-linear models. All chapters are motivated by real-life data, the same data sets that professional forecasters examine. Model simulations, visualization, and engagement with real data are classroom-tested features that facilitate learning.
Hyndman, R.J. and Athanasopoulos, G., Forecasting Principles and Practice, 3rd ed., OTexts, 2021. This textbook is intended to provide a comprehensive introduction to forecasting methods and present enough information about each method for readers to use them sensibly. We don’t attempt to give a thorough discussion of the theoretical details behind each method, although the references at the end of each chapter will fill in many of those details. The entire book is available online and free-of-charge.
Hyndman, R.J., Koehler A.B., Ord, J.K., Snyder, R.D., Forecasting with Exponential Smoothing: The State Space Approach, Springer-Verlag, 2008. Exponential smoothing methods have been around since the 1950s, and are still the most popular forecasting methods used in business and industry. Recently, exponential smoothing has been revolutionized with the introduction of a complete modeling framework incorporating innovations state space models, likelihood calculation, prediction intervals and procedures for model selection. In this book, all of the important results are brought together in a coherent manner with consistent notation. In addition, many new results and extensions are introduced and several application areas are examined in detail.
Levenbach, Hans and Cleary, James P., Forecasting: Practice and Process for Demand Management, Thomson/Brooks-Cole, 2006. This text introduces students to the forecasting principles, applications, and methods of demand forecasting. L&C stress applications in their book, presenting concepts in the context of real examples drawn from their own broad experience as forecasting practitioners in industry, consultants to organizations, and educators. It also addresses the macroeconomic forecasting procedures used by economists as well as the specific product-level forecasting techniques now widely used by corporate sales and operations planning organizations—providing comprehensive coverage of traditional and advanced forecasting tools. Throughout, the authors focus more on training students to perform accurate data analysis than on modeling sophistication. The text incorporates computing throughout the book, featuring Microsoft® Excel applications and including a free copy of anl Excel add-in, called PEERForecaster.xla and data sets on CD.
Makridakis, Spyros, Wheelwright, Steven and Hyndman, Rob, Forecasting: methods and applications, John Wiley and Sons, 1998 (third edition). This book, sometimes known as the “Bible of forecasting”, is the best-selling introductory text in business forecasting. It covers the full range of major forecasting methods and provides a complete description of their essential characteristics including the steps needed for their practical application. The book avoids getting bogged down in the theoretical details that are not essential to understanding how the various methods work, and provides a systematic comparison of the advantages and drawbacks of various methods so that the most appropriate method can be selected for each forecasting situation. It covers a comprehensive set of forecasting horizons (from the immediate to the long term) and approaches to forecasting (time series, explanatory, judgemental, mixed).
Nash, John and Mary, Practical Forecasting for Managers, Arnold Publisher, 2001. This book is an introductory guide to business forecasting for managers and MBA students.
Wright, George and Goodwin, Paul, eds., Forecasting with Judgment, John Wiley and Sons, 1998. This collection of ten papers brings together recent research on the role of judgment in forecasting and methods for improving its application.
Automatic Forecasting Systems features David Reilly’s AutoBox, an automatic ARIMA-modelling system.
Business Forecast Systems, Inc. is the publisher of Dr. Robert Goodrich’s Forecast Pro.
Data Perceptions develops and distributes Prophecy, a multi-user sales forecasting solution for Windows which integrates volume and financial forecasting in a single, easy to use application. It is particularly suited to helping business forecasters develop better, more defensible judgemental forecasts and business plans.
Delphus Inc. features PEER Planner®, a forecast support system for demand analysis and replenishment planning, as well as a PEERForecaster, an Excel Add-in for ad-hoc time series forecasting applications based on the ARIMA and exponential smoothing modeling techniques.
Demand Planning provides consulting and training in Demand Planning, Sales Forecasting, business forecasting and S&OP.
Demand Works offers the Smoothie(tm) suite of solutions for forecasting, demand planning and supply policy optimization. Smoothie’s forecasting features a multi-dimensional technology called Pivot Forecasting(tm) that dynamically aggregates and synchronizes adjustments to any product, plant, channel or other dimensions in real time.
R is a popular, free and open source statistical software. There are numerous packages focussing on time series analysis and forecasting. Here is a comprehensive list.
SAS Institute provides a variety of forecasting and econometrics tools, as well as large-scale automatic forecasting, as part of their comprehensive analytics framework.
Scientific Computing Associates Corp. develops the SCA Statistical System for cross-industry applications in forecasting and time series analysis. Automatic modeling and forecasting for both univariate and multivariate time series make it suitable for large-scale implementations.
Smart Software, Inc. develops and distributes SmartForecasts for Windows, a sales and demand forecasting software system used by manufacturing, marketing, and financial planners worldwide.
Tableau Software forecasting capability allows you to estimate future values by projecting data values based on the historical data. Tableau 8.0 provides built-in statistical models to forecast your data including models that account for seasonality and trends.
Technology Futures, Inc. offers technology forecasting guides and technology forecasts.
Disclaimer: The brief descriptions were provided by the developers. The fact that programs are listed does not imply endorsement by the IIF.