8. The forecast of demand for the next period
is assumed to be equal to the actual demand
in the current period.
9. Uses an average of ‘n’ most recent periods of
demand data to forcast the next period
demand.
For example, it is often used in technical
analysis of financial data, like stock prices,
returns or trading volumes. It is also used in
economics to examine gross domestic
product, employment or other
macroeconomic time series.
10. In exponential smoothing older data is given
progressively-less relative weight
(importance) whereas newer data is given
progressively-greater weight.
Next period’s forecast=this period’s
forecast+α (this period’s actual demand –this
period’s forecast)
11. Based on the assumption that the variable
to be forecast (dependent variable) has
cause-and-effect relationship with one or
more other (independent) variables.
12. The factors responsible for the past trends in
variables to be projected (e.g. sales and
demand) will continue to play their part in
future in the same manner and to the same
extend as they did in the past in determining
the magnitude and direction of the variable.
13.
14. A technique in which a straight line is fitted
to a set of data points to measure the effect
of a single independent variable. The slope
of the line is the measured impact of that
variable.
