Discussion five 1
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Purdue University *
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Course
GB513
Subject
Geography
Date
Feb 20, 2024
Type
docx
Pages
6
Uploaded by CommodoreNewtMaster43
Forecast need
Does Washington or Illinois have the highest percentage of inclement weather? My organization is consistently trying to collect data in inclement weather. Inclement weather can include rain, snow, and low visibility. My organization is trying to collect the most amount of inclement weather data as fast as possible. There are two locations available for this to happen where my company has already created flight teams. The want to capitalize on what area will have the highest prediction for bad weather and they plan to send all their operators to that location. In order to reach yearly kingpin goals this, request for forecast data needs to be met before the end of the quarter.
Forecast situation analysis.
Catego
ry
Dimension
Questions
Answer to the Questions
Time
Span
Is the forecast period a
present, short/medium, or long-
term projection?
Short-term
Urgency
Is the forecast needed immediately?
Yes, forecast can be produced quickly once programmed and past data are available. Frequency
Are frequent forecast Yes, forecast can be
updates needed?
systematically
updated easily. Resour
ce
Math skills
Are quantitative skills limited?
Yes, a fundamental competency level is required. Computer
Are computer capabilities limited?
Yes, a computer is essential for all cases. Financial
Are only limited financial resources available?
No, if data is readily available, out of pocket cost
are minimal.
Input
Antecedent
Are only limited past data available?
No but extended history is helpful with initial development
Variability
Does the primary series fluctuate substantially?
Yes, wide fluctuations are frequently
a significant concern. Internal consistency
Are significant changes in management decisions
expected?
Yes, and of changes are recognized adjustments can be made. External consistency
Are significant environmental changes expected?
Yes, because forecast quality can vary
substantially. External stability
Are significant shifts expected among variable relationships?
Yes, predictive accuracy is weakened if shifts a occur. Output
Detail
Are component forecasts required?
Yes, useful breakdowns on amount of weather would be helpful Accuracy
Is a high level of accuracy critical?
No, if there is a projection of
weather the details are as important. Capability for reflecting direction changes
Should turning points be reflected properly?
No, can be readily adjusted if recognized but long-time horizon often precludes the need. Capability for detecting direction changes
Should turning points be identified early?
Yes, early turning point identification can be a strength under dynamic conditions we would be able to swap locations. Form
Is an internal or Probabilistic
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probabilistic forecast critical?
forecast would
be critical because weather is always a predictive distribution. Recommendations
This is where you describe your selection and demonstrate your understanding, so answer all questions fully.
Answer the following:
1. Which forecasting methodology listed in the article is the best match to your situation?
Adaptative filtering 2. In which categories is the methodology showing a good fit (in other words, why did you select this methodology)?
“Adaptative filtering is a derivation of a weighted combination of actual and estimated outcomes, systematically altered to reflect data pattern changes.” (Georgoff 1986) Weather patterns are constantly changing and what is predicted most times need to be compared to what actually happened. With places like Washington that are known for high precipitation rates it will be important to compare the predicted rain fall vs actual rain fall. Same goes for Illinois in regard to snow. Two strengths to this methodology are that it deals very well with systematic shifts in variables and that it absorbs random fluctuations
and adjust to systematic shifts. Since this is span will be short term it is essential that the forecasting method is
adaptable. Weather is consistently shifting and there are variables that often fluctuate such as location and timing.
3. In which categories does this methodology show a weak fit?
Adaptative filtering shows a weak fit because a computer is essential (note: this document is almost 40 years old in
today’s age everyone has computer in their pocket, so I do not see this as a weak fit at all) and past history is essential although detail and extent vary. I think back in 1986 when this was published these two statements would say this methodology was a weak fit however it is quite easy to get a 10-5 year forecast on weather locations these days. But since I had to put weakness in for the assignment those are the two that this article thought were “relevant”. 4. Describe how this forecast will be executed: Who will do it, where will the data come from, how frequently will it be repeated, and how will the results be used?
My company has a scheduling team who frequently look at weather OI. This team would just need to watch what weather is predicted vs what is really happening in both our Washington and our Illinois location. I think each site should audit weather forecast weakly for two months and
see how much of what was predicted. Weather underground, a free resource, has historical data about US locations dating all the way back to the 1930’s it goes not only by year and month but also by date. The forecast would just need to be compared to weather underground and see its accuracy on past events. Once weather undergrounds accuracy is figured out my team can then compare all three together to get a better picture of what weather was predicted, is happening and has happened.
References
Georgoff, D. M., & Murdick, R. G. (1986). Manager's guide to forecasting.
Harvard Business Review
,
64
(1), 110–120.
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