MANGT 515 Prospective Students
Sample Syllabus
Sample Lesson
Introduction
2.1 Reasons for Forecasting in Project Management
2.2 Forecasting Methods for Projects
2.2.1 Qualitative Forecasting Methods
2.2.2 Quantitative Forecasting Methods
Time Series Analysis
Short Term Forecasting Methods
Naïve Approach
Simple Averages
Moving Averages
Exponential Smoothing
Example of Exponential Smoothing
Trend-Adjusted Exponential Smoothing
Example of Trend-Adjusted Exponential Smoothing
2.3 Intermediate Term Forecasting
Example of Linear Regression
Evaluating the "Fit" of the Regression Line
2.3.2 The Correlation Coefficient (r)
2.3.3. The Standard Error of the Estimate (syx)
2.3.4 Limitations in Forecasting Using Linear Regression
2.3.4 Limitations in Forecasting Using Linear Regression (continued)
2.4 Forecasting the Project End Conditions
2.5 S-curve Forecasting
2.6 Technological Forecasting*

Trend-Adjusted Exponential Smoothing

The exponential smoothing approach discussed above is an appropriate forecasting technique, if the time series exhibits a horizontal pattern (i.e. No trend) with random fluctuations. However, if the time-series exhibits trend, forecasts based on simple exponential smoothing will lag the trend. In such cases, a variation of simple exponential smoothing called the trend-adjusted Exponential smoothing can be used as a forecasting technique. "The trend-adjusted forecast (TAF) has two components:

  1. A smoothed error
  2. A trend factor

    3. TAFt = St-1 + Tt-1 , where

    St-1 = Previous period smoothed forecast

    Tt-1 = Previous period trend estimate

    TAFt = Current period's trend-adjusted forecast

    St = TAFt + alpha(At - TAFt)

    Tt = Tt-1 + beta(TAFt - TAFt-1 - Tt-1), where alpha and betaare smoothing constants

In order to use this method, one must select values of alpha and beta (usually through trial and error) and make a starting forecast and an estimate of the trend" (Stevenson, 2005).