For the Unit VIII assignment, please refer to Section 5.4 of the t.docx
1. For the Unit VIII assignment, please refer to Section 5.4 of the
text.
Monica works at a regional weather office on the Atlantic coast.
She notes (from the office records) that hurricanes have made
landfall on the coast somewhere near their city of Johnstown in
the past 11 years. Monica notes that forecasted landfall has been
different from actual observed landfall as shown in the table
below.
YEAR YEAR ACTUAL (MILES FROM JOHNSTOWN)
24-HOUR FORECAST (MILES FROM JOHNSTOWN)
1 4 6
2 30 5
3 2 40
4 12 10
5 7 13
6 11 5
7 21 11
8 12 25
9 12 8
10 6 15
11
How accurate has the forecast been? Do you think this
difference matters to a beach town? Can you develop a
forecasting system model that may be more accurate?
Explain your methodology and ideas in a paper of at least four
pages. Be sure to research sources to support your ideas, and
integrate the sources using APA-formatted citations and
matching reference lists. Additionally, use Times New Roman
12pt. double-spaced font.
13. 7 160 150
8 190 160
9 200 190
10 190 200
11 — 190
TABLE 5.1 – Computing the Mean Absolute Deviation (MAD)
• Forecast based on
naïve model
• No attempt to adjust
for time series
components
YEAR
ACTUAL
SALES OF
WIRELESS
SPEAKERS
FORECAST
SALES
ABSOLUTE VALUE OF
ERRORS (DEVIATION),
(ACTUAL – FORECAST)
1 110 —
19. TABLE 5.2
MONTH ACTUAL SHED SALES 3-MONTH MOVING
AVERAGE
January 10
February 12
March 13
April 16
May 19
June 23
July 26
August 30
September 28
October 18
November 16
December 14
January —
(12 + 13 + 16)/3 = 13.67
(13 + 16 + 19)/3 = 16.00
(16 + 19 + 23)/3 = 19.33
23. 3-MONTH WEIGHTED
MOVING AVERAGE
January 10
February 12
March 13
April 16
May 19
June 23
July 26
August 30
September 28
October 18
November 16
December 14
January —
[(3 X 13) + (2 X 12) + (10)]/6 = 12.17
[(3 X 16) + (2 X 13) + (12)]/6 = 14.33
[(3 X 19) + (2 X 16) + (13)]/6 = 17.00
68. 6 110 140 +30 +35 35 85 14.2 +2.5
MAD =
forecast errorå
n
=
85
6
= 14.2
Tracking signal =
RSFE
MAD
=
35
14.2
= 2.5 MADs
For Period 6:
Adaptive Smoothing
• Computer monitoring of tracking signals
and self-adjustment if a limit is tripped
• In exponential smoothing, the values of
detects an excessive amount of