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Content1. Simple average method2. Moving average method3. Weighted average method4. Cumulative average method
TOPIC IIІ. FORECASTING BASED ON AVERAGE
METHODS
According to this method, the forecast value is equal to the actual value observed during the last period. So, the naive forecast for any period simply projects the previous period’s actual value.
The naive forecast can be simply the sales from the last period, or for seasonal items, what was sold last year in the same period.
For example, if demand for product was 100 units last week, the naive forecast for the upcoming week is 100 units. If demand in the upcoming week turns out to be 75 units, then the forecast for the following week would be 75 units.
Simplest (naive) forecasting method is based on assumption “what happened last time (last year, last month, yesterday) will
happen again this time”
Average refers to the sum of data for certain periods divided by number of these periods. Forecast based on simple average is calculated by adding all available data and then dividing this total by the number of time periods.
Simple average (SA) is a measure of the mean of a data set
n
yy
n
ii
SA
1ˆ
Statistics on company's profit for 5 years
Company's profit forecast for 2014 based on simple average equals:
thousand $.
For example, statistics on company's profit for 5 years is given in the table 1. Find the company's profit forecast
for 2014 based on simple average method.
6,1265
142136128122120ˆ 2014
y
Moving average method helps to make forecast when from all statistical information we take into account the last few data points, for example, data for the last three, four, five or six periods.
Three-period moving average is defined as a value found by adding numerical data for three periods and then dividing by three.
Four-period moving average is defined as a value found by adding numerical data for four periods and then dividing by four.
Five-period moving average is defined as a value found by adding numerical data for five periods and then dividing by five.
Moving average method
For example, statistical data on demand for 10 months are given in the table 2. Find the average meanings of demand
(three-period moving average)
Forecast based on moving average for the next period equals the average meaning of the recent actual data points for the last several periods.
Forecast based on three-period moving average is calculated by summarizing data for the last three periods and then dividing by three.
Forecast based on four-period moving average is calculated by summarizing data for the last four periods and then dividing by four.
Forecast based on five-period moving average is calculated by summarizing data for the last five periods and then dividing by five.
Moving average method
Demand forecast for January based on
three-period moving average equals:
Demand forecast for January based
on four-period moving average equals:
Demand forecast for January based
on five-period moving average equals:
Demand forecast for January based on moving average
$7,1513
148162145
MAу
$5,1484
148162145139MA
у
$4,1455
148162145139133
MAу
Weighted average is the mean of a set of numbers in which some elements of the set carry more importance (weight) than others. The weighted average multiplies each data point by an arbitrary “weight” and divides by the sum of the weights.
Forecast based on weighted average method is defined as a value found by multiplying each data point by weight factor and then this total dividing by the sum of weights.
Weighted average method
n
ii
n
iii
WA
b
byy
1
1ˆ
Statistics on demand for 8 months
Demand forecast for November equals:
For example, statistical data on demand for 8 months is given in the table 3. Find the demand forecast for
November.
$38,27935,025,02,03,015,025,01,02,0
35,038625,03352,02843,026515,024225,01851,01982,0227ˆ
WAy
Cumulative average method helps to make forecast by taking into account data for the last reporting period and forecasted absolute increase.
Forecast based on cumulative average is calculated by summarizing the last data point and the forecasted
absolute increase.
Cumulative average method
1
^
ˆ nnMMA yyу
Forecasted absolute increase is defined by multiplying the absolute increase for each time period by the corresponding cumulative coefficient. And then the result is summed by the formula:
Cumulative coefficient is calculated by the formula
Cumulative average method
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^
... ykykykyky nnnnnnn
n
tk
Statistics on company’s profit for 5 months
The cumulative coefficient (k) for March:
for April: for May:
for June: for July:
Cumulative average method
067,05
333,011
n
tk
133,05
333,022
k 200,0
5
333,033
k
266,05
333,044
k 333,0
5
333,055
k
Calculation results
Forecasted absolute increase equals:
Forecast of company’s profit for August based on cumulative average equals:
Cumulative average method
664,20067,04133,022,04266,02333,01
^
ny
664,99664,297ˆ ММАу
