Choosing an Appropriate Forecasting Model
Choosing an Appropriate Forecasting Model
Forecasting is done by monitoring changes that occur over time and projecting into the future. Forecasting is commonly used in both the for-profit and not-for-profit sectors of the economy. There are two common approaches to forecasting: qualitative and quantitative. Qualitative forecasting methods are especially important when historical data are unavailable. Qualitative forecasting methods are considered to be highly subjective and judgmental. Quantitative forecasting methods make use of historical data. The goal of these methods is to use past data to predict future values. The following guidelines are provided for determining the adequacy of a particular forecasting model. These guidelines are based on a judgment of how well the model fits the data and assume that you can use past data to predict future values of the time series:
• Performing a Residual Analysis
Residual analysis visually evaluates these assumptions and helps you to determine whether the regression model that has been selected is appropriate. The residual, or estimated error value, is the difference between the observed and predicted values of the dependent variable for a given value of residual appears on a scatter plot as the vertical distance between an observed value and the prediction line. After fitting a particular model to a time series, you plot the residuals over the n time periods.
• Measuring the Magnitude of the Residuals through Squared or Absolute Differences
If, after performing a residual analysis, you still believe that two or more models appear to fit the data adequately, you can use additional methods for model selection. Numerous measures based on the residuals are available. In regression analysis we used the standard error of the estimate. For a particular model, this measure is based on the sum of squared differences between the actual and predicted values in a time series. If a model fits the time-series data perfectly,
Forecasting is done by monitoring changes that occur over time and projecting into the future. Forecasting is commonly used in both the for-profit and not-for-profit sectors of the economy. There are two common approaches to forecasting: qualitative and quantitative. Qualitative forecasting methods are especially important when historical data are unavailable. Qualitative forecasting methods are considered to be highly subjective and judgmental. Quantitative forecasting methods make use of historical data. The goal of these methods is to use past data to predict future values. The following guidelines are provided for determining the adequacy of a particular forecasting model. These guidelines are based on a judgment of how well the model fits the data and assume that you can use past data to predict future values of the time series:
• Performing a Residual Analysis
Residual analysis visually evaluates these assumptions and helps you to determine whether the regression model that has been selected is appropriate. The residual, or estimated error value, is the difference between the observed and predicted values of the dependent variable for a given value of residual appears on a scatter plot as the vertical distance between an observed value and the prediction line. After fitting a particular model to a time series, you plot the residuals over the n time periods.
• Measuring the Magnitude of the Residuals through Squared or Absolute Differences
If, after performing a residual analysis, you still believe that two or more models appear to fit the data adequately, you can use additional methods for model selection. Numerous measures based on the residuals are available. In regression analysis we used the standard error of the estimate. For a particular model, this measure is based on the sum of squared differences between the actual and predicted values in a time series. If a model fits the time-series data perfectly,