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ISYE 6501 Analytic Model Homework 3 Georgia Institute of Technology
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ISYE 6501 Analytic Model Homework 3 Georgia Institute of Technology
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ISYE 6501 Analytic ModelHomework 3 Georgia Institute of
Technology.
, lOMoAR cPSD| 43283024
homework3_isye6501
Question 7.1
Question:
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 α (the first
smoothing parameter) to be closer to 0 or 1, and why?
Answer:
Any problem trying to forecast response data over time would be appropriate for an exponential smoothing
model. In my work experience, I have tried to develop a time series based forecast on retail sales by day
based on the current trend and seasonality. Exponential smoothing could have been used to deliver these
forecasts. All the data we need would be our response retail sales by day for a long time horizon, preferably
back more than two years.
The tuning of the alpha parameter would depend on what the goal of the forecasts are. If providing a
estimate and the baseline - de-seasonalized forecast - I would choose a value closer to 1 to smooth out the
fit as much as possible. Other applications, like using the forecasts to generate actions in other systems,
may need a less smooth forecast to account for the typical fluctuations.
Question 7.2
Question:
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. (Part of the point of this assignment is for you to think
about how you might use exponential smoothing to answer this question. Feel free to combine it with other
models if you’d like to. There’s certainly more than one reasonable approach.)
Answer:
I will apply the Holt Winter exponential smoothing algorithm to analyze if our temperature data has any
overall trends in the end of summer temperature in the last 20 years.
To do this first first read in the data and format it as a time series object to feed the Holt Winters algorithm. I
also quickly look at the temperatures by year to see if I can visibly inspect any trends. By looking at the plot
of temperature over time - nothing stands out as a trend, randomness can fluctuation seem like a constant
pattern.
The base implementation of Holt Winters in the stats packages automatically fits a triple exponential
smoothing model, with overall smoothing, trend smoothing, and seasonality smoothing parameters (Alpha,
Beta, Gamma respectively).
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