GLINA ARTS
A FORECAST ON FALL SESSION ENROLLMENT
ALA AL-LOZI
ANTHONY
ALEXANDER
MATTHEW COKER
TOREY HERZOG
PROBLEM
 Apply statistical techniques to the Glina Arts data
provided to predict demand for courses and course
media types for Fall2016
 Agenda
1. How the problem was approached
2. What was considered
3. Final 2016 Fall enrollment prediction
4. Recommendations
5. Conclusions
APPROACH
Step One: Decide what forecasting method to predict the
demand of Fall session 2016
 Exponential Triple Smoothing
 Time Series – Registration Date
 Demand – Fall Session Enrollment
APPROACH
Step Two: Evaluated how registration date connected
to fall session enrollment
• Adult Dataset:
August 15, 2014 to
November 18, 2014
• Camps Only Dataset:
August 18, 2014 to
November 21, 2014
• Conclusion:
Registration from 3rd
week of August to 3rd
week in November
indicates count of Fall
Session enrollees
APPROACH
Step Three: Create a pivot table to obtain a total number of individuals enrolled in
each course media type
 Organize pivot table were a count of enrollees per week is indicated by course
media type
APPROACH
Step Four: Enter the enrollee count to its corresponding week in a new dataset
that includes every week of the original data range to each course media type of
Adult Classes and Camps Only
APPROACH
Step Five: Run a Exponential Triple Smoothing
Forecast to each new course media type worksheets
from Adults Classes and Camps Only in Excel 2016
WHAT WAS CONSIDERED
Models
 Linear Regression
 Would not have given the total number of
people registered by course or course
media type
 Exponential Single Smoothing
 Suitable for forecasting data with no trend
or seasonal pattern
 Exponential Triple Smoothing
 Time series forecast – appropriate for
dataset
 Considers Seasonality
 Easy to use one Excel 2016
TRIPLE EXPONENTIAL
SMOOTHING
This method is comprised of several statistics that are required for accurate
forecasting:
 Alpha
 Data Smoothing Factor
 Desired Level: ≤0.05
 Probability of predicting Type I error
 Beta
 Trend Smoothing Factor
 Desired Level: ≤0.20
 Probability of predicting Type II error
 Gamma
 Seasonal change smoothing factor
 Ordinal data correlation
TRIPLE EXPONENTIAL
SMOOTHING
This method is comprised of several statistics that are required for
accurate forecasting:
 Mean Absolute Scaled Error (MASE)
 Measures the accuracy of the forecasts
 Symmetric Mean Absolute Percentage Error (SMAPE)
 Measures the accuracy based on the error percentage
 Mean Absolute Error (MAE)
 Measures how close the forecasts are to the eventual outcomes
 Root Mean Square Error (RMSE)
 Measures the average of the square roots of the errors and deviations
WHAT WAS CONSIDERED
Demand
 Course
 Only small numbers of people
enrolled by course - no trend
would have been seen
using ETS by course
 Course Media Type
 More appropriate way to see a
trend compared to evaluating
each course
8/18/2013
9/22/2013
10/27/2013
12/1/2013
1/5/2014
2/9/2014
3/16/2014
4/20/2014
5/25/2014
6/29/2014
8/3/2014
9/7/2014
10/12/2014
11/16/2014
12/21/2014
1/25/2015
3/1/2015
4/5/2015
5/10/2015
6/14/2015
7/19/2015
8/23/2015
9/27/2015
11/1/2015
12/6/2015
1/10/2016
2/14/2016
3/20/2016
4/24/2016
5/29/2016
7/3/2016
8/7/2016
9/11/2016
10/16/2016
11/20/2016
FINAL PREDICATION
Adult Classes – Woodturning
25
20
15
10
5
0
-5
-10
-15
-20
-25
Registration Count
Forecast(Registration Count)
Lower Confidence Bound(Registration Count)
Upper Confidence Bound(Registration Count)
8/18/2013
9/22/2013
10/27/2013
12/1/2013
1/5/2014
2/9/2014
3/16/2014
4/20/2014
5/25/2014
6/29/2014
8/3/2014
9/7/2014
10/12/2014
11/16/2014
12/21/2014
1/25/2015
3/1/2015
4/5/2015
5/10/2015
6/14/2015
7/19/2015
8/23/2015
9/27/2015
11/1/2015
12/6/2015
1/10/2016
2/14/2016
3/20/2016
4/24/2016
5/29/2016
7/3/2016
8/7/2016
9/11/2016
10/16/2016
11/20/2016
FINAL PREDICATION
Camps Only – Metals
30
20
10
0
-10
-20
-30
Registration Count
Forecast(Registration Count)
Lower Confidence Bound(Registration Count)
Upper Confidence Bound(Registration Count)
FINAL PREDICATION
FINAL PREDICATION
FINAL PREDICATION
CONCLUSION
 Adults Classes
 No courses had an Alpha ≤ 0.05
 Camps Only
 “Camps” is the only course with an Alpha ≤ 0.05
The data is not very reliable. It is difficult to make
an accurate prediction.
RECOMMENDATIONS
 Provide Current Data
 Current data will allow a more accurate prediction
 Dataset is of low quality

Significant cleaning was made in past data project.
 Can conclude there are possible data entry errors
that may have effected the results of the forecasting analysis

Implement more stringent governance of data

Train data collectors on how to properly gather clean data

Consider creating a locked Excel document with dropdowns
 Clarify “No Season Specified” entry under “Season”

Majority of “No Season Specified” entries fell under the second week of August 2013
to the third week of November 2013

Entries appear to be Fall Session data, but the group felt uncomfortable making this
assumption
QUESTIONS