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The Exponential Smoothing Model Is Given By y^t+1=αyt+(1α)y^t\hat { y } _ { t + 1 } = \alpha y _ { t } + ( 1 - \alpha ) \hat { y } _ { t }

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The exponential smoothing model is given by y^t+1=αyt+(1α)y^t\hat { y } _ { t + 1 } = \alpha y _ { t } + ( 1 - \alpha ) \hat { y } _ { t } where y^t+1= forecast of sales for period t+1\hat { y } _ { t + 1 } = \text { forecast of sales for period } t + 1 yt= actual sales for period ty _ { t } = \text { actual sales for period } t y^t= forecast of sales for period t\hat { y } _ { t } = \text { forecast of sales for period } t α= smoothing constant, 0α1\alpha = \text { smoothing constant, } 0 \leq \alpha \leq 1 This model is used to predict the future based on the past data values.
a. The observed values with the smoothing constant a = 0.45 are given in the below table. The third column of the table displays the forecast values obtained using the above model. The forecasted error ytyt^y _ { t } - \hat { y _ { t } } is calculated in the fourth column, and the square of the forecast error and the sum of squared forecast errors are given in fifth column. Construct this table in your spreadsheet model using the formula above. (Hint: The first forecast value is same as the observed value.)
Alpha = 0.45 Squared Forecast  Day  Observed Value  Forecast  Forecast Error  Error 11515.000.00000.000021215.003.00009.000031413.650.35000.122541813.814.192517.577151515.690.69410.481861615.380.61820.3822715.665.340028.515982118.060.06300.004091818.036.034636.4169101215.323.680913.5494111916.985.024525.2458122219.245.236527.4211131416.884.119916.9737142118.731.26601.6026152019.300.30370.0922 Sum of the  squared forecast 177.39 error \begin{array}{ccccc}&&&&\text {Squared Forecast }\\\text { Day } & \text { Observed Value } & \text { Forecast } & \text { Forecast Error } & \text { Error } \\\hline 1 & 15 & 15.00 & 0.0000 & 0.0000 \\\hline 2 & 12 & 15.00 & -3.0000 & 9.0000 \\\hline 3 & 14 & 13.65 & 0.3500 & 0.1225 \\\hline 4 & 18 & 13.81 & 4.1925 & 17.5771 \\\hline 5 & 15 & 15.69 & -0.6941 & 0.4818 \\\hline 6 & 16 & 15.38 & 0.6182 & 0.3822 \\\hline 7 & 15.66 & 5.3400 & 28.5159 \\\hline 8 & 21 & 18.06 & -0.0630 & 0.0040 \\\hline 9 & 18 & 18.03 & -6.0346 & 36.4169 \\\hline 10 & 12 & 15.32 & 3.6809 & 13.5494 \\\hline 11 & 19 & 16.98 & 5.0245 & 25.2458 \\\hline 12 & 22 & 19.24 & -5.2365 & 27.4211 \\\hline 13 & 14 & 16.88 & 4.1199 & 16.9737 \\\hline 14 & 21 & 18.73 & 1.2660 & 1.6026 \\\hline 15 & 20 & 19.30 & -0.3037 & 0.0922 \\\hline& \text { Sum of the } \\&\text { squared forecast } \\177.39 & \text { error } \\\hline\end{array}
b. The value of á is often chosen by minimizing the sum of squared forecast errors. Use Excel Solver to find the value of á that minimizes the sum of squared forecast errors.

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Definitions:

R

In electrical engineering, "R" typically refers to resistance, which is a measure of the opposition to the flow of electric current.

Apparent Power

The product of the current and voltage in an AC circuit, measured in volt-amperes, not necessarily equal to the actual power consumed.

RC Parallel Circuit

An electrical circuit in which a resistor and a capacitor are connected in parallel, affecting the impedance and phase angle of the overall circuit.

R

A symbol commonly used to represent resistance in an electrical circuit, measured in ohms.

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