AA7004P - MGT 780 – (EXERCISE 4) Forecasting

QUESTION [5-33] A major source of revenue in Texas is a state sales tax on certain types of goods and
services. Data are compiled and the state comptroller uses them to project future revenues for the state
budget. One particular category of goods is classified as Retail Trade. Four years of quarterly data (in
$millions) for one particular area of southeast Texas follow:

Quarte Year 1 Year 2 Year 3 Year 4

r
1 218 225 234 250
2 247 254 265 283
3 243 255 264 289
4 292 299 327 356

Before forecasting the sales, a regression analysis has been run in order to generate an equation to
forecast the future sales. The analysis is as below:
Standard
Coefficients Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 281.5625 5.753032 48.94159 3.18E-14 268.9002 294.2248 268.9002 294.2248
TIME 3.69375 0.428806 8.614042 3.21E-06 2.749955 4.637545 2.749955 4.637545
Q1 -75.6688 5.574474 -13.5742 3.25E-08 -87.9381 -63.3994 -87.9381 -63.3994
Q2 -48.8625 5.491392 -8.89802 2.34E-06 -60.949 -36.776 -60.949 -36.776

Q3 -52.0563 5.440934 -9.56752 1.15E-06 -64.0317 -40.0808 -64.0317 -40.0808

Equation: ŷ = 281.5625 + 3.69375(T) – 75.6688(Q1) – 48.8625(Q2) – 52.0563(Q3)

Regression Statistics
0.98424
Multiple R 4
0.96873
R Square 6
Adjusted R 0.95736
Square 8
7.67070
Standard Error 9
Observations 16

ANOVA
Significanc
df SS MS F eF
85.2110
Regression 4 20055.2 5013.8 7 3.34E-08
647.237 58.8397
Residual 11 5 7
20702.4
Total 15 4
 AA7004P - MGT 780 – (EXERCISE 4) Forecasting

The model shows the R Square value is 96%. The model can explain 96% of the sales. This indicates that
the variation of the Texas State revenue is proportionally explained by the independent variable of the
regression model which are the time, quarter 1, quarter 2 and quarter 3 values.

The Significance F analysis on the sales model also shows that it has a value of 3.34 x 10 -08. As this value
is less than 0.05, this means that the sales model generated is a significant model.

Sales – Forecast Table

YEAR QUARTER SALES TIME Q1 Q2 Q3 FORECAST FE MAD MSE MAPE

1 1 218 1 1 0 0 209.59 8.41 8.41 70.77 3.86
1 2 247 2 0 1 0 240.09 6.91 6.91 47.78 2.80
1 3 243 3 0 0 1 240.59 2.41 2.41 5.82 0.99
1 4 292 4 0 0 0 296.34 -4.34 4.34 18.81 1.49
2 1 225 5 1 0 0 224.36 0.64 0.64 0.41 0.28
2 2 254 6 0 1 0 254.86 -0.86 0.86 0.74 0.34
2 3 255 7 0 0 1 255.36 -0.36 0.36 0.13 0.14
2 4 299 8 0 0 0 311.11 -12.11 12.11 146.71 4.05
3 1 234 9 1 0 0 239.14 -5.14 5.14 26.39 2.20
3 2 265 10 0 1 0 269.64 -4.64 4.64 21.51 1.75
3 3 264 11 0 0 1 270.14 -6.14 6.14 37.67 2.32
3 4 327 12 0 0 0 325.89 1.11 1.11 1.24 0.34
4 1 250 13 1 0 0 253.91 -3.91 3.91 15.31 1.56
4 2 283 14 0 1 0 284.41 -1.41 1.41 2.00 0.50
4 3 289 15 0 0 1 284.91 4.09 4.09 16.71 1.41
4 4 356 16 0 0 0 340.66 15.34 15.34 235.24 4.31
Total 0.00 4.86 40.45 1.77
Sales – Forecast Graph

By looking at the Sales Forecast graph above, it shows that the sales were in an uptrend seasonal.
Through a judgmental prediction, it shows that the sales will be declining after period 16. Which means,
the sales will decline on the first quarter of the 5 th year and start incline on second quarter onwards. The
graph also shows that the forecast trending is close to the actual trend despite the slight difference
 AA7004P - MGT 780 – (EXERCISE 4) Forecasting

during period 8 where the forecasted sales were slightly higher than the actual sales and period 16
where the actual sales were slightly higher than the forecasted sales. Nevertheless, the forecast of this
model is correctly forecasting the actual value of the sales. Thus, the prediction for the future revenue of
the Texas state can be based on the forecasted value.

Tracking Model

TIME SALES FORECAST FE RSFE lFEl Cumulative lFEl MAD Tracking

1 218 209.59 8.41 8.41 8.41 8.41 8.41 1.00
2 247 240.09 6.91 15.32 6.91 15.32 7.66 2.00
3 243 240.59 2.41 17.74 2.41 17.74 5.91 3.00
4 292 296.34 -4.34 13.40 4.34 22.07 5.52 2.43
5 225 224.36 0.64 14.04 0.64 22.71 4.54 3.09
6 254 254.86 -0.86 13.17 0.86 23.57 3.93 3.35
7 255 255.36 -0.36 12.81 0.36 23.94 3.42 3.75
8 299 311.11 -12.11 0.70 12.11 36.05 4.51 0.15
9 234 239.14 -5.14 -4.44 5.14 41.19 4.58 -0.97
10 265 269.64 -4.64 -9.08 4.64 45.82 4.58 -1.98
11 264 270.14 -6.14 -15.21 6.14 51.96 4.72 -3.22
12 327 325.89 1.11 -14.10 1.11 53.07 4.42 -3.19
13 250 253.91 -3.91 -18.01 3.91 56.98 4.38 -4.11
14 283 284.41 -1.41 -19.43 1.41 58.40 4.17 -4.66
15 289 284.91 4.09 -15.34 4.09 62.48 4.17 -3.68
16 356 340.66 15.34 0.00 15.34 77.82 4.86 0.00
Generated Tracking Graph:

Tracking
6.00

4.00

2.00

0.00
0 2 4 6 8 10 12 14 16 18

-2.00

-4.00

-6.00
 AA7004P - MGT 780 – (EXERCISE 4) Forecasting

By referring to the Tracking Graph above, the upper limit was set at tracking value of +$4 million while
the lower limit was set at -$4million. This is because ± 4 million is less than 5% from the total overall
budget. Thus, if the forecast is within this limit, it still workable for the state of Texas as it does not run
out from their expectation more than 5%. Throughout 16 period, the revenue of the Texas is within this
control boundaries. However, on the period 14, the revenue was slightly off the lower limit. This shows
that on the 2nd quarter of the 4th year, the Texas state might have faced a short decline in their economy
that caused its revenue to decline.

Thus, using the Equation: ŷ = 281.5625 + 3.69375(T) – 75.6688(Q1) – 48.8625(Q2) – 52.0563(Q3)

The forecast for year 5 is as below:

QUARTE FORECAS
YEAR R TIME Q1 Q2 Q3 T
5 1 17 1 0 0 268.69
5 2 18 0 1 0 299.19
5 3 19 0 0 1 299.69
5 4 20 0 0 0 355.44

Since the state of Texas already have this data, they should diversify their portfolio of income so that
their income would not follow any quarterly trend that based on weather condition, thus helping them
to generate constant income throughout the year.
 AA7004P - MGT 780 – (EXERCISE 4) Forecasting

QUESTION [5-35] Trevor Harty, an avid mountain biker, always wanted to start a business selling top-of-
the-line mountain bikes and other outdoor supplies. A little over 6 years ago, he and a silent partner
opened a store called Hale and Harty Trail Bikes and Supplies. Growth was rapid in the first 2 years, but
since that time, growth in sales has slowed a bit, as expected. The quarterly sales (in $1,000s) for the
past 4 years are shown in the table below:
Year 1 Year 2 Year 3 Year 4
Quarter 1 274 282 282 296
Quarter 2 172 178 182 210
Quarter 3 130 136 134 158
Quarter 4 162 168 170 182
Before forecasting the sales, a regression analysis has been run in order to generate an equation to
forecast the future sales. The analysis is as below:
Standard
Coefficients Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 150 4.76063403 31.50840814 3.90893E-12 139.5219151 160.4780849 139.5219151 150
TIME 2.05 0.35483671 5.777305282 0.00012332 1.269009667 2.830990333 1.269009667 2.05
Q1 119.15 4.612877233 25.82986583 3.38566E-11 108.9971257 129.3028743 108.9971257 119.15
Q2 19.1 4.544127079 4.203227522 0.001477526 9.098443734 29.10155627 9.098443734 19.1
-
Q3 -28.95 4.502373112 6.429942451 4.8749E-05 -38.8596564 -19.0403436 -38.8596564 -28.95

Equation: ŷ = 281.5625 + 3.69375(T) – 75.6688(Q1) – 48.8625(Q2) – 52.0563(Q3)

Regression Statistics
Multiple R 0.995391776
R Square 0.990804788
Adjusted R Square 0.987461075
Standard Error 6.347512039
Observations 16

ANOVA
Significance
df SS MS F F
296.318704 4.06547E-
Regression 4 47755.8 11938.95 9 11
40.2909090
Residual 11 443.2 9
 AA7004P - MGT 780 – (EXERCISE 4) Forecasting

Total 15 48199

The model shows the R Square value is 99%. The model can explain 99% of the sales a value that very
close to 100%. This indicates that the variation of the Hale & Harty Trail Bikes and Supplies is
proportionally explained by the independent variable of the regression model which are the time,
quarter 1, quarter 2 and quarter 3 values. The Significance F analysis on the sales model shows a value of
4.07 x 10-11. As this value is less than 0.05, this means that the sales model generated is a significant
model.

Sales – Forecast Table

Variables
Year Quarter Sales Time Q1 Q2 Q3 Forecast FE MAD MSE MAPE
1 1 274 1 1 0 0 271.2 2.8 2.8 7.84 1.021898
1 2 172 2 0 1 0 173.2 -1.2 1.2 1.44 0.697674
1 3 130 3 0 0 1 127.2 2.8 2.8 7.84 2.153846
1 4 162 4 0 0 0 158.2 3.8 3.8 14.44 2.345679
2 1 282 5 1 0 0 279.4 2.6 2.6 6.76 0.921986
2 2 178 6 0 1 0 181.4 -3.4 3.4 11.56 1.910112
2 3 136 7 0 0 1 135.4 0.6 0.6 0.36 0.441176
2 4 168 8 0 0 0 166.4 1.6 1.6 2.56 0.952381
3 1 282 9 1 0 0 287.6 -5.6 5.6 31.36 1.985816
3 2 182 10 0 1 0 189.6 -7.6 7.6 57.76 4.175824
3 3 134 11 0 0 1 143.6 -9.6 9.6 92.16 7.164179
3 4 170 12 0 0 0 174.6 -4.6 4.6 21.16 2.705882
4 1 296 13 1 0 0 295.8 0.2 0.2 0.04 0.067568
4 2 210 14 0 1 0 197.8 12.2 12.2 148.84 5.809524
4 3 158 15 0 0 1 151.8 6.2 6.2 38.44 3.924051
4 4 182 16 0 0 0 182.8 -0.8 0.8 0.64 0.43956
Total -7.1E-14 4.1 27.7 2.294822

Sales – Forecast Graph

Sales - Forecast Graph

350
300
250
200
150
100
50
0
0 2 4 6 8 10 12 14 16 18
Sales Forecast
 AA7004P - MGT 780 – (EXERCISE 4) Forecasting

By looking at the graph, it shows that the sales are in a consistent trend seasonal. Through a judgmental
prediction, it is shows that the sales will be inclining after period 15 which is the first and second quarter
of the 5th year and then decline during the third and fourth quarter of the fifth year. The decline can be
explained by people doing less outdoor activities such as biking during the cold weather (winter)
assuming that quarter 1 start on 1 st of January of every year. The graph also shows that the forecast
trending is close to the actual trend. However, there is a slight difference on period 11 where the actual
sales declined slightly more than predicted. Nevertheless, the forecast of this model is still correctly
forecasting the actual value of the sales. Thus, the prediction for the future sales of the Hale & Harty Trail
Bikes & Supplies can be based on the forecasted value.

Tracking Model

TIME SALES FORECAST FE RSFE lFEl Cumulative lFEl MAD Tracking

1 274 271.2 2.8 2.80 2.8 2.8 2.8 1.00
2 172 173.2 -1.2 1.60 1.2 4 2 0.80
3 130 127.2 2.8 4.40 2.8 6.8 2.266666667 1.94
4 162 158.2 3.8 8.20 3.8 10.6 2.65 3.09
5 282 279.4 2.6 10.80 2.6 13.2 2.64 4.09
6 178 181.4 -3.4 7.40 3.4 16.6 2.766666667 2.67
7 136 135.4 0.6 8.00 0.6 17.2 2.457142857 3.26
8 168 166.4 1.6 9.60 1.6 18.8 2.35 4.09
9 282 287.6 -5.6 4.00 5.6 24.4 2.711111111 1.48
10 182 189.6 -7.6 -3.60 7.6 32 3.2 -1.13
11 134 143.6 -9.6 -13.20 9.6 41.6 3.781818182 -3.49
12 170 174.6 -4.6 -17.80 4.6 46.2 3.85 -4.62
13 296 295.8 0.2 -17.60 0.2 46.4 3.569230769 -4.93
14 210 197.8 12.2 -5.40 12.2 58.6 4.185714286 -1.29
15 158 151.8 6.2 0.80 6.2 64.8 4.32 0.19
16 182 182.8 -0.8 0.00 0.8 65.6 4.1 0.00

Generated Tracking Graph:

 AA7004P - MGT 780 – (EXERCISE 4) Forecasting

Tracking Graph
6.00

4.00

2.00

0.00
0 2 4 6 8 10 12 14 16 18

-2.00

-4.00

-6.00

By referring to the Graph, the upper limit was set at tracking value of +$4 thousand while the lower limit
was set at -$4 thousand. Throughout 16 period, the revenue of the Hale & Harty Trail Bikes & Supplies is
within this control boundaries. This is because ± 4 thousand are less than 5% from the total overall
budget. Thus if the forecast is within this limit, it still workable for the Hale & Harty Trail Bikes & Supplies
as it does not run out from their expectation more than 5%. However, on the period 12 & 13, the
revenue was slightly off the lower limit. This shows that on the 2 nd quarter of the 3rd year that Hale &
Harty Trail Bikes & Supplies might facing a short decline in their overall financial that caused by their
revenue/sale are declining during that particular period.

In my opinion, Hale & Harty Trail Bikes & Supplies should use this information collected to analyse a
different strategy such as diversifying their products or other marketing/promotion strategy in order to
cover up for the sales loss during winter season. This is enable them to at least boost the sales towards
an upward trend instead of remaining a consistent seasonal trend.
 AA7004P - MGT 780 – (EXERCISE 4) Forecasting

QUESTION [5-39] The following table provides the Dow Jones Industrial Average (DJIA) opening index
value on the first working day of 1994-2013: Develop a trend line and use it to predict the opening DJIA
index value for years 2014, 2015, and 2016. Find the MSE for this model.

Naïve Model

Period Year DJIA Forecast FE MAD MSE MAPE

0 1994 3754 -
1 1995 3834 3754 80 80 6400 0.02
2 1996 5117 3834 1283 1283 1646089 0.25
3 1997 6448 5117 1331 1331 1771561 0.21
4 1998 7908 6448 1460 1460 2131600 0.18
5 1999 9213 7908 1305 1305 1703025 0.14
6 2000 11502 9213 2289 2289 5239521 0.20
7 2001 10791 11502 -711 711 505521 0.07
8 2002 10022 10791 -769 769 591361 0.08
9 2003 8342 10022 -1680 1680 2822400 0.20
10 2004 10453 8342 2111 2111 4456321 0.20
11 2005 10784 10453 331 331 109561 0.03
12 2006 10718 10784 -66 66 4356 0.01
13 2007 12460 10718 1742 1742 3034564 0.14
14 2008 13262 12460 802 802 643204 0.06
15 2009 8772 13262 -4490 4490 20160100 0.51
16 2010 10431 8772 1659 1659 2752281 0.16
 AA7004P - MGT 780 – (EXERCISE 4) Forecasting

17 2011 11577 10431 1146 1146 1313316 0.10

18 2012 12392 11577 815 815 664225 0.07
19 2013 13104 12392 712 712 506944 0.05
20 2014 13104
21 2015
22 2016
Total 9350 1304.316 2634861 14.09

By comparing of all model, Naïve model shows the best model to forecast the DJIA or normally been
refer to stock market index. This is based on comparing the MAD, MSE and MAPE value. Below shows
the graph comparing the forecast value of all model.

Comparison of All Model

Model Comparison Graph

14000

12000

10000

8000

6000

4000

2000

0
1990 1995 2000 2005 2010 2015 2020
DJIA Naive 2Y MA 2Y WMA ES

Graph 1.0: Model Comparison Graph

 AA7004P - MGT 780 – (EXERCISE 4) Forecasting

By doing simple comparison between DJIA with the forecast value for each model, Naïve model still
remain as the best model to forecast DJIA.

Actual – Forecast Graph (Based on Naïve model)

Sales - Forecast Graph

14000

12000

10000

8000

6000

4000

2000

0
1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014
DJIA 3754 Naive

Graph 2.0: Sales – Forecast Graph

 AA7004P - MGT 780 – (EXERCISE 4) Forecasting

Trending Graph - Naive Model

12

10

6 Naive
Axis Title Linear (Naive)
4

0
f(x) =2000 2005 2010 2015 2020
1990 1995
R² = 0
Axis Title

Graph 3.0: Sales – Forecast Trending Graph

Therefore,

Forecast equation: Y = 397.37x – 786,978 where R^2 = 0.6666

Tracking Model

Cumulative
Period Year DJIA Naive FE RSFE lFEl lFEl MAD Tracking
1 1995 3834 3754 80 80 80 80 80.00 1.00
2 1996 5117 3834 1283 1363 1283 1363 681.50 2.00
3 1997 6448 5117 1331 2694 1331 2694 898.00 3.00
4 1998 7908 6448 1460 4154 1460 4154 1038.50 4.00
5 1999 9213 7908 1305 5459 1305 5459 1091.80 5.00
6 2000 11502 9213 2289 7748 2289 7748 1291.33 6.00
7 2001 10791 11502 -711 7037 711 8459 1208.43 5.82
8 2002 10022 10791 -769 6268 769 9228 1153.50 5.43
9 2003 8342 10022 -1680 4588 1680 10908 1212.00 3.79
10 2004 10453 8342 2111 6699 2111 13019 1301.90 5.15
11 2005 10784 10453 331 7030 331 13350 1213.64 5.79
12 2006 10718 10784 -66 6964 66 13416 1118.00 6.23
13 2007 12460 10718 1742 8706 1742 15158 1166.00 7.47
14 2008 13262 12460 802 9508 802 15960 1140.00 8.34
15 2009 8772 13262 -4490 5018 4490 20450 1363.33 3.68
16 2010 10431 8772 1659 6677 1659 22109 1381.81 4.83
17 2011 11577 10431 1146 7823 1146 23255 1367.94 5.72
 AA7004P - MGT 780 – (EXERCISE 4) Forecasting

18 2012 12392 11577 815 8638 815 24070 1337.22 6.46

19 2013 13104 12392 712 9350 712 24782 1304.32 7.17
20 2014 13722.55 13104 618.55 9968.55 618.55 25400.55 1270.03 7.85
21 2015 14119.92 13722.55 397.37 10365.92 397.37 25797.92 1228.47 8.44

Generated Tracking Graph:

Tracking Graph
9.00
8.00
7.00
6.00
5.00
4.00
3.00
2.00
1.00
0.00
0 5 10 15 20 25

Graph 4.0: Tracking Graph

Based on the tracking graph, it shows that this model have ± 8 in the result of their forecasting. Due to
rapid changes of the stock market, the forecast tracking should be at least around ± 1 as the investor are
risking their capital and off forecast result will make things worse and also make the forecast unreliable
to the investor. Forecasting data should be a helping tools to an investor or even industries to evaluate
the stock market situation.

To forecast more accurately, having an expectation of how the stock market might perform is important
in giving you a base for making your investment decisions. Using different analysis techniques and
comparing the results will improve the accuracy of the forecasting.

Based on the calculation, the forecast for 2014, 2015 and 2016 is as below:

Period Year Forecast

13325.1
20 2014
8
13722.5
21 2015
5
14119.9
22 2016
2
 AA7004P - MGT 780 – (EXERCISE 4) Forecasting

Question 3-42: The following table gives the average monthly exchange rate between the U.S.
dollar and the euro for 2009. It shows that 1 euro was equivalent to 1.289 U.S. dollars in January
2009. Develop a trend line that could be used to predict the exchange rate for 2010. Use this
model to predict the exchange rate for January 2010 and February 2010.

Naïve Model
Tim
e Month EX.Rate Forecast FE MAD MSE MAPE
1 January '09 1.289
2 February '09 1.324 1.289 0.035 0.0350 0.0012 2.64
3 March '09 1.321 1.324 -0.003 0.0030 0.0000 0.23
4 April '09 1.317 1.321 -0.004 0.0040 0.0000 0.30
5 May '09 1.28 1.317 -0.037 0.0370 0.0014 2.89
6 June '09 1.254 1.28 -0.026 0.0260 0.0007 2.07
7 July '09 1.23 1.254 -0.024 0.0240 0.0006 1.95
8 August '09 1.24 1.23 0.01 0.0100 0.0001 0.81
9 September '09 1.287 1.24 0.047 0.0470 0.0022 3.65
10 October '09 1.298 1.287 0.011 0.0110 0.0001 0.85
11 November '09 1.283 1.298 -0.015 0.0150 0.0002 1.17
 AA7004P - MGT 780 – (EXERCISE 4) Forecasting

12 December '09 1.311 1.283 0.028 0.0280 0.0008 2.14

13 January '10 1.311
14 February '10
Total 0.022 0.0218 0.0007 1.70

By comparing of all model, Naïve model shows the best model to forecast the future exchange rate. This
is based on comparing the MAD, MSE and MAPE value. Below shows the graph comparing the forecast
value of all model.

Comparison of All Model

1.34
1.32
1.3
1.28
1.26
1.24
1.22
1.2
1.18
0 2 4 6 8 10 12 14 16
EX.Rate Naïve 2m.MA 2m.WMA ES

Graph 1.0: Model Comparison Graph

Actual – Forecast Graph (Based on Naïve model)

 AA7004P - MGT 780 – (EXERCISE 4) Forecasting

Actual - Forecast Graph

1.34
1.32
1.3
1.28
1.26
1.24
1.22
1.2
1.18
0 2 4 6 8 10 12 14 16
EX.Rate Forecast

Graph 2.0: Sales – Forecast Graph

Forecast Trending Graph

Forecast
12

10

6 Forecast
Axis Title Linear (Forecast)
4

0
0 f(x)
2 =4 6 8 10 12 14 16
R² = 0
Axis Title

Graph 3.0: Sales – Forecast Trending Graph

Therefore,

Forecast equation: Y = -0.002x + 1.3014 where R^2 = 0.6666

Tracking Model

Time Month EX.Rate Forecast FE RSFE lFEl Cumulative lFEl MAD Tracking
 AA7004P - MGT 780 – (EXERCISE 4) Forecasting

1 January '09 1.289

2 February '09 1.324 1.289 0.035 0.035 0.035 0.035 0.0175 2.00
3 March '09 1.321 1.324 -0.003 0.032 0.003 0.038 0.012667 2.53
4 April '09 1.317 1.321 -0.004 0.028 0.004 0.042 0.0105 2.67
5 May '09 1.28 1.317 -0.037 -0.009 0.037 0.079 0.0158 -0.57
6 June '09 1.254 1.28 -0.026 -0.035 0.026 0.105 0.0175 -2.00
7 July '09 1.23 1.254 -0.024 -0.059 0.024 0.129 0.018429 -3.20
8 August '09 1.24 1.23 0.01 -0.049 0.01 0.139 0.017375 -2.82
September
9 '09 1.287 1.24 0.047 -0.002 0.047 0.186 0.020667 -0.10
10 October '09 1.298 1.287 0.011 0.009 0.011 0.197 0.0197 0.46
November
11 '09 1.283 1.298 -0.015 -0.006 0.015 0.212 0.019273 -0.31
December
12 '09 1.311 1.283 0.028 0.022 0.028 0.24 0.02 1.10
13 January '10 1.311
14 February '10

Generated Tracking Graph:

Tracking
12.00

10.00

8.00

6.00

4.00

2.00

0.00
0 2 4 6 8 10 12 14 16

Graph 4.0: Tracking Graph

There are numerous methods of forecasting exchange rates, likely because none of them have been
shown to be superior to any other. This speaks to the difficulty of generating a quality forecast. Based on
this model, is shows that the model have ± 3 in the result of their forecasting. Throughout 12 period, the
average monthly exchange rate between the U.S. dollar and the euro was within this normal range.
However, on July 2009, the average monthly exchange rate went off the lower limit. Due to hostile and
rapid changes of the world currency, the forecast tracking should be at least around ± 1. This is because
even a slightly off forecasting can turn around the entire business plan for one company or even a
 AA7004P - MGT 780 – (EXERCISE 4) Forecasting

nation. Therefore, in order to get a better forecasting, it is crucial to reduce the uncertainty and gather as
much information because the more data we have the less likely we will be caught in uncertainties. This
will help us to generate the best way to forecasting and get an accurate and reliable forecasting result to
the actual amount.

Thus, based on the calculation the forecast for Jan’ 10 and Feb’ 10 is as below:

Time Month Forecast

13 January '10 1.2754
14 February '10 1.2734