How to Best Understand Forecasting Methods

Executive Summary

  • What are forecasting methods?
  • How the forecasting methods are used differently between consensus and statistical forecasting.

Forecasting Methods Definition

A forecasting method is the category of forecasting which is being applied.

  • Time series forecasting (such as exponential smoothing, and moving average)
  • Consensus-based forecasting (the Delphi method, and prediction markets) are also methods.
  • Causal forecasting is a method of forecasting where independent variables are selected in order to predict dependent variables.

Forecasting methods can be confused with a forecasting model, which is most often used to describe the specific mathematical formula used to develop a forecasting in either time series or causal forecasting. The model is differentiated from the forecasting parameters which then control the model in its development of the forecast.

How is a Forecast Method Different from a Forecast Model?

Forecasting models are the specific or exact procedure that was used to create the forecast. A three-period moving average is a model while a two-period moving average is another model, both of which are within the time series forecasting method.

Forecasting models are subordinate to, or children of parent forecasting methods.

Forecasting models are strongly associated with statistical forecasting, and not consensus-based forecasting or collaborative forecasting.

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Reference

Forecast models and parameters for time series are covered in the following book.

Forecasting Parameters Book

Forecast Parameters

Uses of Forecast Parameters

Statistical forecasting is one of the major approaches to creating forecasts across many different fields. Statistical forecasting has proven unmatched in the ability to cost effectively create enormous numbers of items, and the statistical forecast is often the first forecast which is created – although manual adjustments may follow.

The Need to Understand Forecast Parameters

However, in many statistical forecasting systems, it is necessary to understand the parameters, which control the forecasting model and to tune it for the specific items to which it applied. This book not only explains the statistical forecast parameters (dividing them into three parameter categories) but also shows many examples of parameters and how the parameters affect the different forecasting examples.
 By reading this book you will:
  • Understand the different categories of forecast parameters.
  • How different statistical forecasting applications work with forecast parameters (learn the difference between manually set and internally set forecast parameters and so-called best-fit forecasting)
  • Learn how changes in forecast parameters create changes to the forecast produced.
  • How to compare and contrast forecast parameters to understand better the forecast profiles which a company uses.

Chapters

  • Chapter 1: Introduction
  • Chapter 2: Where Forecasting Fits within the Supply Chain Forecasting Footprint
  • Chapter 3: The Common Problems with Statistical Forecasting
  • Chapter 4: Forecast Parameters
  • Chapter 5: Introduction to Best-Fit Forecasting
  • Chapter 6: Conclusion

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