Year Month Time Period COGS

$11121637$

$2225506$

$3326594$

$4421933$

$5523117$

$6626979$

$7728337$

$8828926$

$9925015$

$101028196$

$111124170$

$121225004$

$211324575$

$21435848$

$31535217$

$41632759$

$51736542$

$61836109$

$71940279$

$82037816$

$92136338$

$102237814$

$112339359$

$122434151$

$312539862$

$22650664$

$32748533$

$42845382$

$52943981$

$63041276$

$73144284$

$83246920$

$93347469$

$103443187$

Consider COGS.xlsx$.$ Apply the following forecasting methods to forecast Year $4.$ Implement each model in a separate spreadsheet.

Plot the data. How would you describe the data in terms of seasonality and trend?

Moving average using k $=12.$

Weighted moving average using k $=12($find wi using solver$)$

Exponential Smoothing $($find $\backslash$alpha using solver$)$

Stationary Additive seasonal effect $($find $\backslash$alpha and $\backslash$beta using solver$)$

Stationary Multiplicative seasonal effect $($find $\backslash$alpha and $\backslash$beta using solver$)$

Double moving average $($k$=12)$

Holt's method

Holt$-$Winter's method for additive seanonal

Holt$-$Winter's method for multiplicative seanonal

Seasonal Regression

Which model is the best for this dataset? Compare each method based on MSE.