In forecasting by exponential smoothing, if α is a smoothing constant, then:

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1 Answer

answered Feb 23, 2022 by Kartikpandey (95.6k points)
selected Feb 23, 2022 by Ritikgupta

Best answer

Correct Answer - Option 1 : New Forecast = α (latest sales figure) + (1 - α ) (old forecast)

Explanation:

Forecasting is defined as estimating the future value that a parameter will take. Most scientific forecasting methods forecast the future value using past data.
Some simple forecasting models using time series data are simple average, moving average and simple exponential smoothing.

Moving average Method: or rolling average Method:

In this method, fresh average is calculated at the end of each period by adding the actual demand data for the most recent period and deleting the data for the order period. It gives equal weight to each of the most recent observations.
\({F_{n+1}} = \frac{{{D_1} + {D_2} + {D_3} + {D_4} + \ldots \ldots \ldots \ldots \ldots \ldots + {D_n}}}{n}\)

Weighted moving average Method:

This method gives unequal weight to each demand data with more weight to recent data.
\({F_{n+1}} = \left[ {{w_{1}} \times {D_{1}} +{w_{2}\times {D_{2}}} +..........+ {w_{n}} \times {D_{n}}} \right]\)

Exponential Smoothing Method:

This method gives weight to all the previous data and the pattern of weight assigned is exponentially decreasing in order with most recent data is given the highest weight.
In exponential smoothing method of forecast, the forecast for the next period is equal to
Ft = α Dt-1 + (1 - α) Ft-1
where, D_t-1 = latest figure sale or latest demand, F_t-1 = old forecast
If we further expand the expression
Ft = α Dt-1 + (1 - α) (α Dt-2 + (1-α) Ft-2
Ft = α Dt-1 + α (1-α ) Dt-2 + (1 - α )2 Ft-2
- If smoothing coefficient (α) is 1 then the latest forecast would be equal to the previous period actual demand.
- The technique is not as simple as compared to the moving average method.
- All observations are not assigned equal weightage