There are several assumptions about forecasting:

**DEMAND FORECASTING METHODS**

There are
several assumptions about forecasting:

1. There is
no way to state what the future will be with complete certainty. Regardless of
the methods that we use there will always be an element of uncertainty until
the forecast horizon has come to pass.

2. There
will always be blind spots in forecasts. We cannot, for example, forecast
completely new technologies for which there are no existing paradigms.

3.
Providing forecasts to policy-makers will help them formulate social policy. The
new social policy, in turn, will affect the future, thus changing the accuracy
of the forecast.

**i.** **OPINION
POLLING METHODS**

**a.** **EXPERTS
OPINION METHOD**

**Genius forecasting **- This
method is based on a combination of intuition, insight, and luck. Psychics and
crystal** **ball readers are the most
extreme case of genius forecasting. Their forecasts are based exclusively on
intuition. Science fiction writers have sometimes described new technologies
with uncanny accuracy

**b.** **CONSUMER
‘S SURVEY METHOD**

In this
method consumer‘s are contacted personally to disclose their future plans

so that
we can able to forecast the future because they are ultimate targeters/buyers

**c.
****COMPLETE
ENUMERATION SURVEY**

Here all
the units of consumers are taken into account without any cutshorts

So here
large number of consumers will be there to get the unbiased information .The
main

Advantage
of this method is its accuracy and its main drawback is it is time consuming
one.

**d.** **SURVEY
METHOD**

Here from
the total population certain number of units will be selected as sample units,
then the opinion collection will be made. This method is less tedious and less
costly than the above method.

**ii.** **STATISTICAL
METHODS**

Fitting
trend line by observation

This
method of estimating trend is elementary,easy and quick.It involves merely
plotting of annual sales on graph and then estimating just by observation where
the trend line lies.

**Trend extrapolation **- These
methods examine trends and cycles in historical data, and then use** **mathematical techniques to extrapolate
to the future. The assumption of all these techniques is that the forces
responsible for creating the past, will continue to operate in the future. This
is often a valid assumption when forecasting short term horizons, but it falls
short when creating medium and long term forecasts. The further out we attempt
to forecast, the less certain we become of the forecast

**b.
****Simulation
methods **- Simulation methods involve using analogs to model complex systems.
These** **analogs can take on several forms.
A mechanical analog might be a wind tunnel for modeling aircraft performance.
An equation to predict an economic measure would be a mathematical analog. A
metaphorical analog could involve using the growth of a bacteria colony to
describe human population growth. Game analogs are used where the interactions
of the players are symbolic of social interactions

**c.
****Trend
Analysis: **Uses linear and nonlinear regression with time as the explanatory
variable, it is used** **where pattern
over time have a long-term trend. Unlike most time-series forecasting
techniques, the Trend Analysis does not assume the condition of equally spaced
time series.

**d.
****Simple
Moving Averages**: The best-known forecasting methods is the moving
averages or simply takes a** **certain
number of past periods and add them together; then divide by the number of
periods. Simple Moving Averages (MA) is effective and efficient approach
provided the time series is stationary in both mean and variance. The following
formula is used in finding the moving average of order n, MA(n) for a period
t+1,

**e.
****Exponential
Smoothing Techniques: **One of the most successful forecasting methods is
the exponential** **smoothing (ES)
techniques. Moreover, it can be modified efficiently to use effectively for
time series with seasonal patterns. It is also easy to adjust for past
errors-easy to prepare follow-on forecasts, ideal for situations where many
forecasts must be prepared, several different forms are used depending on
presence of trend or cyclical variations. In short, an ES is an averaging
technique that uses unequal weights; however, the weights applied to past
observations decline in an exponential manner

**f.
****Least-Squares
Method: **To predict the mean y-value for a given x-value, we need a line which
passes** **through the mean value of
both x and y and which minimizes the sum of the distance between each of the
points and the predictive line. Such an approach should result in a line which
we can call a "best fit" to the sample data. The least-squares method
achieves this result by calculating the minimum average squared deviations
between the sample y points and the estimated line. A procedure is used for
finding the values of a and b which reduces to the solution of simultaneous
linear equations. Shortcut formulas have been developed as an alternative to
the solution of simultaneous equations..

**g.
****Regression
and Moving Average**: When a time series is not a straight line one may
use the moving** **average (MA) and
break-up the time series into several intervals with common straight line with
positive** **trends to achieve linearity
for the whole time series. The process involves transformation based on slope
and then a moving average within that interval. For most business time series,
one the following transformations might be effective.

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Engineering Economics and Financial Accounting : Demand and Supply Analysis : Demand Forecasting Methods |

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