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The forecasting method that will be used here is time series method, a statistical
technique that makes use of historical data accumulated over a period of time. Time
series methods assume that what has occurred in the past will continue to occur in the
future. This
method relates the
forecast to only one
factor, time.
Time
series
method
tends
to
be
most
useful
for
short
range and
possible
to
be
used
for
longer
range
forecasting.
Sometimes
a
time
series
displays
a
steady tendency of increase or decrease
through
time.
Such
a
tendency
is
called
a
trend.
When
we
plot
the
observations
against time,
we
may
notice
that a straight
line can describe the
increase or decrease
in
the
series
as
time
goes
on.
This
should remind of simple linear regression and
indeed,
in
such
cases
we
will
use
the method
of
least
squares
to
estimate
the
parameters
of
a
straight
line
model.
A
simple linear regression equation is
fit to the
data by least squares. A straight line model to account for a trend is of the form:
Zt=Bo+B1t+at
Where
t
is
the
time
and
a
is
the
error
term.
The
coefficient
Bo
and
B1
are
the
regression intercept and slope, respectively.
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