WELCOME TO THE FORECAST DEMO


   The Forecast Demo illustrates the fundamental 
   principles of time-series forecasting. You can
   specify forecasting parameters to apply towards
   generating data.

   A time-series is a sequential collection of data 
   observations, indexed over time. Modeling a
   time-series as a combination of past values and
   residual white noise allows the extrapolation of
   data for future points of time.

   This process is known as FORECASTING and uses an 
   AUTOREGRESSIVE FORECASTING MODEL of ORDER P,
   where P represents the number of past time-series
   values used to compute the forecast. In general, 
   the accuracy of the forecast improves as the value 
   of P increases. 

   The SAMPLE AUTOCORRELATION function is a commonly
   used tool to determine the accuracy of a forecasting
   model.  The autocorrelation of a time-series measures
   the dependence between observations as a function
   of their time differences or LAG. An N-element
   time-series with approximately 95% of its values
   in the interval,

   [-1.96/sqrt(N), 1.96/sqrt(N)]

   is said to be STATIONARY and is the prerequisite
   to an accurate forecast. This interval is displayed
   with dashed lines on the plot of the SAMPLE
   AUTOCORRELATION.

   See the MATHEMATICS section of the on-line help
   for more information.


   MENU OPTIONS
   ------------

   File Menu:
      Select "Quit" to exit the Forecast Demo and return
      to the IDL Demo main screen.

   About Menu:
      Select "About forecasting" for information about 
      the Forecasting Demo.


   FEATURES
   --------

   <<ORDER OF THE MODEL>> slider
      Select the order, P, of the forecasting model.

   <<NUMBER OF FORECASTS>> slider
      Select the number of data points to forecast.
      Each new data point is represented by a red 
      triangle.

   <<GENERATE NEW DATA>> button
      Generate a new set of random values according to 
      set of forecasting parameters specified with the 
      above sliders.