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forecast.txt
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1997-07-08
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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.