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8. Single Exponential Smoothing forecast example ( Enter your problem )
  1. Formula & 3 year Single Exponential Smoothing forecast Example
  2. 4 year Single Exponential Smoothing forecast Example
  3. 5 year Single Exponential Smoothing forecast Example

3. 5 year Single Exponential Smoothing forecast Example





1) 3 year Single Exponential Smoothing forecast
year123456
Sales650700810800900700
Calculate 3 year Single Exponential Smoothing forecast


Solution:
(1)
year
(2)
Sales
(3)
Exponential Smoothing
`(alpha=0.1)`
1650650
2700`0.1*650+0.9*650=650`
3810`0.1*700+0.9*650=655`
4800`0.1*810+0.9*655=670.5`
5900`0.1*800+0.9*670.5=683.45`
6700`0.1*900+0.9*683.45=705.105`
7`0.1*700+0.9*705.105=704.5945`


(1)
year
(2)
Sales
(3)
Exponential Smoothing
(4)
Error
(5)
|Error|
(6)
`"Error"^2`
(7)
`|%"Error"|`
1650650
2700650
3810655
4800670.5`800-670.5=129.5``129.5``16770.25``16.19%`
5900683.45`900-683.45=216.55``216.55``46893.9025``24.06%`
6700705.105`700-705.105=-5.105``5.105``26.061``0.73%`
7704.5945Total`351.155``63690.2135``40.98%`


Forecasting errors

1. Mean absolute error (MAE), also called mean absolute deviation (MAD)
MAE`=1/n sum |e_i|=351.155/3=117.0517`


2. Mean squared error (MSE)
MSE`=1/n sum |e_i^2|=63690.2135/3=21230.0712`


3. Root mean squared error (RMSE)
RMSE`=sqrt(MSE)=sqrt(21230.0712)=145.7054`


4. Mean absolute percentage error (MAPE)
MAPE`=1/n sum |e_i/y_i|=40.98/3=13.66`








This material is intended as a summary. Use your textbook for detail explanation.
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