Statistical Software
For Exponential Smoothing we will use the Excel Spreadsheet
in this module. I created the spreadsheet so there is no
copyright; do as you please 
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Why would I need something like Exponential Smoothing?
Before defining what Exponential Smoothing is, here is an
example of a case that needs this wonderful tool:
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Exponential Smoothing Motivation
Suppose you are investigating Alaska Airlines flight delays at Ted
Stevens International in Anchorage, Alaska. Ted Stevens is a vital
connection between Asia and the United States, and is has the third
largest cargo traffic in the United States. Thus there is good reason
why you are concerned with on time arrivals as cargo is often time
dependent.
Your goal is to predict the flight delays for the next three years.
There are many ways to do this, and we have discussed one already:
Using a regression line.
However, sometimes data is not linear, and the data may actual
depend on past data! Would this be a case of that?....Think it over as
we learn Exponential Smoothing.
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Examining the Data
Take a look at the data in the spreadsheet titled
Exponential Smoothing.
This data looks to be quite chaotic, but let’s try
Exponential smoothing to attempt to predict the
next few values.
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Exponential Smoothing (1)
The idea behind Exponential Smoothing is to use
past values to predict the future values, with
more emphasis on the most recent values.
We weight past values, add them together, and
estimate the next value. All the weights must add
up to 1 or 100%
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Exponential Smoothing (2)
For example, using the data in our spreadsheet, let’s
try to predict the number of delays in August 2013
by using 70% of the previous month’s delays and
adding that to 30% of the delays from two months
ago. Thus we would have:
0.3(Di-1) +0.7(i-2)=Di where Di is the number of delays
of the ith month.
See the results on the Smoothing All Data Tab.
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Exponential Smoothing (3)
Reviewing our results, we see that our delays look
to approach 140, and if we keep going, this is the
number of delays we will predict throughout. Also
note that in this model we start with the first two
months data, and then use solely our predictions
from there on out. This is what is taught in most
courses, but obviously does not serve us well in this
case!
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Exponential Smoothing (3)
Reviewing our results, we see that our delays look
to approach 140, and if we keep going, this is the
number of delays we will predict throughout. Also
note that in this model we start with the first two
months data, and then use solely our predictions
from there on out. This is what is taught in most
courses, and has great benefits, but obviously does
not serve us well in this case!
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Exponential Smoothing (4)
Let’s try with just the final six months in our data
set. See the sheet Smoothing with Six Months.
This seems to work better, but I am still not
satisfied. Perhaps we should reexamine the original
data.
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Reexamining the Data
Note that this is Alaska, and we should probably
take into account the time of year. Look back
over the winter delays versus the summer delays;
there seem to be more of a pattern here.
You can use any smoothing technique you see fit
and any model as long as you justify it. I
encourage you to explore other options when
smoothing your data, and do not hesitate to
contact me if you have questions.
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Directions for Smoothing
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3.
4.
Determine what your alpha will be
Put your alpha (between 0 and 1) in the cell G2.
Paste your data column A starting with A1.
Drag the cell B3 all the way down your data
going one or two past your data value (these are
your predicted values-see video).
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Final Thoughts
• Remember to use common sense and examine
the data
• If you use more than two previous values, be
sure all the weights add up to 1.
• If you need help, ask!
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