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FORECASTING

FORECASTING. Types of Forecasts. Qualitative Time Series Causal Relationships Simulation. Qualitative Forecasting Approaches. Historical Analogy Panel Consensus Delphi Market Research. Quantitative Approaches. Naïve (time series) Moving Averages (time series)

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FORECASTING

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  1. FORECASTING

  2. Types of Forecasts • Qualitative • Time Series • Causal Relationships • Simulation

  3. Qualitative Forecasting Approaches • Historical Analogy • Panel Consensus • Delphi • Market Research

  4. Quantitative Approaches • Naïve (time series) • Moving Averages (time series) • Exponential Smoothing (time series) • Trend Projection (time series) • Linear Regression (causal)

  5. Naïve Method • This period’s forecast = Last period’s observation • Crude but effective • August sales = 1000; September sales = ?? • 1000!

  6. Moving Averages • This period’s forecast = Average of past n period’s observations • Example: for n = 3: Sales for Jan through March were 100, 110, 150 • April forecast = (100+110+150)/3 = 120

  7. Example

  8. Evaluating Forecasts • Concept: Forecast worth function of how close forecasts are to observations • Mean Absolute Deviation (MAD) • MAD = sum of absolute value of forecast errors / number of forecasts (e.g. periods) • MAD is the average of the absolute value of all of the forecast errors.

  9. Weighted Moving Averages • This period’s forecast = Weighted average of past n period’s observations • Example: for n = 3: Sales for Jan through March were 100, 110, 150 • Suppose weights for last 3 periods are: .5 (March), .3 (Feb), and .2 (Jan) • April forecast =.5*150+.3*110+.2*100 = 128

  10. Exponential Smoothing • New Forecast = Last period’s forecast + alpha * (Last period’s actual observation - last period’s forecast) • Mathematically: F(t) = F(t-1) + alpha * [A(t-1) - F(t-1)], where F is the forecast; A is the actual observation, and alpha is the smoothing constant -- between 0 and 1 • Example: F(t-1) = 100; A(t-1) = 110; alpha = 0.4 -- Find F(t) • F(t) = 104 • Can add parameters for trends and seasonality

  11. Trend Projections • Use Linear regression • Model: yhat = a + b* x • a = y-intercept: forecast at period 0 • b = slope: rate of change in y for each period x • Example: Sales = 100 + 10 * t, where t is period • For t = 15, Find yhat -- • yhat = 250 • Can find and a and b via Method of Least Squares

  12. Linear Regression • Model: yhat = a + b1 * x1 + b2 * x2 + … + bk * xk • a = y-intercept • bi = slope: rate of change in y for each increase in xi, given that other xj’s are held constant • Example: College GPA = 0.2 + 0.5 HS GPA + 0.001 HS SAT • For a HS student with a 3.0 GPA and 1200 SAT - what is the forecast? • The forecast college GPA = 2.90 • Can find a, b1, and b2 via Method of Least Squares

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