Creating Accurate Forecasts When Your Demand History Includes Outliers

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Preparing forecasts using data that contain one or more unusually large or small demand periods can be challenging. Depending on your forecasting approach, these “outliers” can have a significant impact on your forecasts. This article surveys three different approaches to forecasting data containing unusual demand periods, discusses the pros and cons of each and recommends when it is best to use each approach.

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The Ins and Outs of Using Dynamic Regression Models for Forecasting


The recording of our recent educational one-hour Webinar 
is now available for viewing on-demand

This session demystifies regression modeling, demonstrating how these models can provide insight into your data and why they often yield more accurate results than alternative forecasting methods. Learn:

  • When to apply regression models
  • How to build and diagnose the models
  • How to use leading indicators, lagged vari¬ables, Cochrane-Orcutt terms and “dummy” variables
  • Best practices for applying regression models

You can register now for our next live Webinar How to Effectively Combine Judgment with Statistical Forecasts

Terms Every Forecaster Should Know

101cForecasting is a dynamic and interdisciplinary field that involves a multitude of people, processes and techniques. Forecasters are constantly building on their current pool of knowledge in order to improve their processes, drawing on whatever resources they can find. A forecaster’s best resource, however, is often other forecasters—but it can be difficult to share information if everyone is speaking a different language. For this post we’ve compiled and defined a list of terms that every forecaster should be familiar with. Continue reading