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We calculated a Neural Network Model which is an iterative fitting optimization that identifies an optimal point between a hold out sample and the model building database.
The optimal convergence point
Model vs. Actual
Time series in broken into a trend and cyclical components. The series below is an extreme cyclical one where the peaks are generally underestimated.
Missed peaks can be corrected by adding a constant
ImpactsThis application locates the insignificant factor within our data
Since Season has a greater impact than Time, Time is the insignificant factor
Cross SectionThis determines how our target will be effected by changing the value of one factor
As the month changes, so does the trans amt.This graph is cyclical in nature and shows that throughout the summer months the trans
amt peaks
3-D Cross SectionDetermines what will happen to our target if two factors were simultaneously changed
It’s a combination of a cyclical and a trend modelThis shows that as we change time and season than we can see how the trans amt has
changed
ReasoningBy reasoning we are able to deifier all the insignificant factors throughout our data
The unexplained box shows that aprox. 30,000 effects are insignificant
Bar ChartDetermines how predictive our model is by showing our error
We can do this by finding the difference of the model chart from the actual chart
SummaryThis is the word summary of the statistical value of our data
AnomaliesViews and Locates outliners within our data
That is the data that doesn’t fit patterns within the cycle or trendIn this particular application, the peaks show where error occurred
DifferencesOverall model error
In order to obtain overall model error we subtract the model from the actual value
By carefully analyzing the results, we were able to determine that the trans amount values from hotels with an economy status are the
highest in the summer months. Though we determined that the trans amount increases throughout the summer months, we also
discovered that there has been a steady decrease in the trans amount from year 2000 when it peaked- to the present day low
Conclusion
Economy
Luxury
Mid-scale with F&B
Mid-scale w/o F&B
Missing
Upper Upscale
Upscale