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ISYE 6501 Introduction to Analytic Model Homework 3 Georgia Institute of Technology.
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ISYE 6501 Introduction to Analytic Model Homework 3 Georgia Institute of Technology.
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ISYE 6501 Introduction to AnalyticModel Homework 3 Georgia
Institute of Technology.
, lOMoARcPSD|43283024
ISYE 6501 HW 3
HT
2024-06-03
Question 7.1 Describe a situation or problem from your job, everyday life, current events, etc., for which
exponential smoothing would be appropriate. What data would you need? Would you expect the value of
alpha (the first smoothing parameter) to be closer to 0 or 1, and why?
As a former healthcare worker, a situation where exponential smoothing might be appropriate is to forecast
monthly hospital visits. This can be helpful for resource planning and for staffing/inventory management.
The data we would need is the historical monthly patient visits, information on any known seasonal variations
(e.g., flu season), and other special events that have affected admissions in the past (e.g., COVID-19 pandemic,
natural disasters, etc.).
I would expect that alpha is closer to 0 – perhaps between 0.2-0.5. An alpha closer to 0 gives more weight
to observations of the more distant past. In other words, I would anticipate that the data has long-term
trends that are generally stable over time. For example, routine checkups and chronic disease management
both involve regular, stable visits, so it seems appropriate to rely on observations that are further in the
past. This could change, however, depending on the type of visit. A more volatile set of data would perhaps
be something like emergency department visits, which would be more spontaneous.
Question 7.2 Using the 20 years of daily high temperature data for Atlanta (July through October) from
Question 6.2 (file temps.txt), build and use an exponential smoothing model to help make a judgment of
whether the unofficial end of summer has gotten later over the 20 years.
Because we have worked with this data before and we generally expect that weather data to contain trend
and seasonality data, I chose to use the Holt-Winters model for the example below. ** please note, I did not
test for outliers.
temp_data <- read.table("week 3 data-summer/temps.txt", header=TRUE)
## flatten the data
temp_unlist <- as.vector(unlist(temp_data[,-1]))
##convert to time series object
temp_ts <- ts(temp_unlist, start = c(1996, 1), end = , frequency = 123)
plot.ts(temp_ts)
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