Chapter 11: Forecasting Models â 2007 Pearson Education Forecasting Forecasting is attempting to predict the future • Decision makers want to reduce uncertainty by predicting future values such as sales or investment return Steps in Forecasting Determine the objective of the forecast Identify items to be forecast Determine time horizon Select the forecasting model(s) Gather data Validate model Make forecast and implement results Types of Forecasts Qualitative - subjective methods based on intuition and experience Time Series – based on historical data and assume the past indicates the future Causal Models – data based where there may be a cause and effect relation between variables Qualitative Forecasting Models Delphi Method – an iterative group process where a group of experts attempt to reach consensus Jury of Executive Opinion – uses opinions of high level managers often combined with statistical models Qualitative Forecasting Models Sales Force Composite – each salesperson estimates sale in his/her own region and forecast are combined for an overall forecast Consumer Market Survey – future purchase plans are solicited from customers Measuring Forecast Error Measures how accurate the forecast was For time period t: Forecast error = Actual value – Forecast value = At - Ft Methods of Measuring Overall Forecast Error • Mean Absolute Deviation (MAD) MAD = ∑ |At – Ft| / T where T = the number of time periods • Mean Squared Error (MSE) MSE = ∑ (At – Ft) / T Methods of Measuring Overall Forecast Error • Mean Absolute Percent Error (MAPE) Measure error as a percent of actual values MAPE = 100 ∑ [ |At – Ft| / At ] / T Time Series • • A time series is where the same value is recorded at regular time intervals Examples: daily stock price, monthly sales, annual revenue, etc Correlation Coefficient Examples Coefficient of Determination (R2) • Measures the proportion of variation in the dependent variable (Y) that can be explained with the independent variable (X) 0