Question 1 contains the actual values for 12 periods (listed in order, 1-12). In

Question

Question 1 contains the actual values for 12 periods (listed in order, 1-12). In Excel, create forecasts for periods 6-13 using each of the following methods: 5 period simple moving average; 4 period weighted moving average (0.63, 0.26, 0.08, 0.03); exponential smoothing (alpha = 0.23 and the forecast for period 5 = 53); linear regression with the equation based on all 12 periods; and quadratic regression with the equation based on all 12 periods.  Round all numerical answers to two decimal places.

Question 1:

The actual values for 12 periods (shown in order) are:

(1) 45  (2) 52 (3) 48 (4) 59  (5) 55  (6) 57  (7) 64  (8) 58  (9) 68  (10) 66  (11) 72  (12) 75

Using a 5 period simple moving average, the forecast for period 13 will be:

Question 2:

Using the 4 period weighted moving average, the forecast for period 13 will be:

Question 3:

With exponential smoothing, the forecast for period 13 will be:

Question 4:

With linear regression, the forecast for period 13 will be:

Question 5:

With quadratic regression, the forecast for period 13 will be:

Question 6:

Considering only the forecasts for period 6-12, what is the lowest MAD value for any of the methods?

Explanation / Answer

Using R

> library(forecast)
> library(TTR)

Q1)

> # Q1)SMA model
> x=c(1,2,3,4,5,6,7,8,9,10,11,12)
> y=c(45,52,48,59,55,57,64,58,68,66,72,75)
> df1=data.frame(y)
> df1=ts(df1)
> mod1=SMA(df1,5)
> forecasteddf <- forecast(mod1, 1)
> forecasteddf
Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
13 69.81297 69.45478 70.17116 69.26516 70.36078

Using a 5 period simple moving average, the forecast for period 13 will be 69.81

Q2

> # Weighted
> wt=c(0.63, 0.26, 0.08, 0.03)
> mod2=SMA(df1,5,wts=wt)
> mod2
Time Series:
Start = 1
End = 12
Frequency = 1
[1] NA NA NA NA 51.8 54.2 56.6 58.6 60.4 62.6 65.6 67.8
> forecasteddf <- forecast(mod2, 1)
> forecasteddf
Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
13 69.81297 69.45478 70.17116 69.26516 70.36078

Using a 4 period wighted moving average, the forecast for period 13 will be 69.81

Q4)

> #Linear regression model
> model <- lm(y ~ x )
> forecasteddf <- forecast(model, 1)
.
> forecasteddf
Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
13 46.51282 41.39091 51.63473 38.19591 54.82974

Using linear regaression model , the forecast for period 13 will be 46.51

Q5)

> #quadratic model
> model <- lm(y ~ x + I(x^2))
> forecasteddf <- forecast(model, 1)
> forecasteddf
Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
13 47.16758 41.26751 53.06765 37.51711 56.81806

Using quadratic regaression model , the forecast for period 13 will be 47.16

We will give only 4-bit solution because of chegg rule

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