The document discusses forecasting passenger traffic at Amsterdam Airport Schiphol (AMS) using trend and seasonality models. It aims to set a concurrent target figure based on long-term (21 year) and short-term (3 year) data while fulfilling constraints. Forecasts were generated for 2014 using a power trend model on 21 years of data and seasonality models on 3 years of data. Most segments were accurately forecasted, with only transfers labeled as "fair". The study shows it is possible to design targets using different timescales that provide the same forecast figure, though it requires careful analysis.

1. Keeping The Same Rule !
by Mohammed Salem Awad
Consultant
Date of Issue : 05/03/2014

2. Keeping The Same Rule
By Mohammed Salem Awad
Consultant
One of the main factors for successes is good planning, especially when we plan for futures as to design
objectives and set targets, but the issue when we plan for targets from some raw data, that may have
concurrent results or figures as the case of AMS airport which is shown in the following table. Really to
set a concurrent figures to get the same target is a hard task, then how to solve this dilemma !!!
As in the following table:
+
+
+

3. Objective :
So, is it possible to keep the same rule for forecasting
figures? i.e to get target that reflects the long term data
base of 21 years and set that target to reflect the
seasonality models of short term data base – three years
period to get the same forecasting design figure provide
that all the addressed trend and seasonality models
fulfills the required constrains, to be a fair forecasting
for the following passengers data base of AMS airport -
i.e Europe – Intercontinental and O&D - Transfer and
Scheduled – Unscheduled as it reported in their reports.
Models
- Trend for 21 years Data Base ( 21 Data Set) – optimum case
- Seasonality Model for 3 years Data Base ( 36 Data Set) – optimum case
- Two seasonlity models for 3 years Data Base ( 72 Data Set ) – optimum case
Constrains
There many constrains that should be fulfill the analysis
1- R2
is greater than 80%
2- Signal Tracking is in the range of - 4 and + 4
3- The forecast trend of 2014, for 21 years data base is the landmark (targeting forecasting figure)
4- All the seasonality forecasting results, should fulfill the above statement.
5- So for forecast traffic passengers of 2014 – should be equal
Trend Forecast
x
Europe
Intercontinental
x
O&D
Transfer
x
Scheduled
Unscheduled
x
x
21
43

4. Trend Forecast:
Based on 21 data set (21 years data base from
1992- 2012)
By implement trend approach using the best of
line fit ( Power Function ) the results of fair
fitting are
R2
= 96.5 while Signal Tracking = ± 5.71
The Forecasting of 2014
= 54,203,771 Passengers
Max/Min Signal Tracking Analysis:
The aim of this analysis is to keep most of the
signal tracking values in constrain band ( -4 and +
4 ) maintaining high value of R2
.
The graph shows the residual values by yellow
color are out of the band for 21 set data base, which reached the highest extreme value by ± 5.71.
Mathematical Model:
The mathematical model is power function with
the following equation
Actual Data for 2013 ( 1-10 ) are not included as
the data of 2013 are not available ( Nov. and Dec).
Amsterdam
Airport Schiphol
1

5. Seasonality Model ( Short Term ) : Europe + Intercontinental =
Generally speaking the normal method to evaluate short range
data with seasonality impacts is AREMA Model, but in this analysis
we will try use the best of art technique that reflect two
parameters only, they are displacement and Rotational, our
approach is to find the line of fit that passing through
the year of accumulated forecasted figures of 12
months for 2014, and that reflects a minimum errors
and high relation factor ( R2
) for both series ( Europe &
Intercontinental ) which satisfies the following relation
Europe + Intercontinental =
= 54,203,771 Passengers
2 x
x
Intercontinental Europe2

6. Origin and Destination Transfer
Scheduled Unscheduled
3
4

7. Keeping The Same Rule
Final Results:
Forecasting Accuracy Matrix:
Forecasting Accuracy Matrix can be represented by four
regions i.e Fair , Mislead, Poor, and Unrelated, for our
cases :
only one case ( Transfer ) is FAIR as it is satisfied
the pre- request
constrains while most
of the other segments
are Mislead which
actually fairs results
that deny the mislead
issue for the following
reasons :
1
2
3
4

8. 1- The Signal Tracking values are defined on both sides of the trend line so the issue of
displacement is not exist.
2- By visual inspection, the forecasted model is lay on the actual data.
Conclusions:
The study shows, that there is possibility to design our targets even though to have same
target, off course it hard task but it needs patience and time to deliver a fine results.
The rule of the signal tracking is to refine the final results and positioning the trend line in the
final direction of analysis.
Two methods can be used to get the forecasted figure of 2014 = = 54,203,771 Passengers
either in one step ( analysis ) based on 72 data set – optimum case which is applied.
Or in two steps ( two analysis ) one optimum and the other one is adjusted based on 36 data set
each.
All data segment are reported, and any researcher can compare the forecasted figure by the
actual data to evaluate the forecasting approach.
The study shows high accuracy.

Contact:
Mohammed Salem Awad
Consultant
Tel: 00 967 736255814
Email: smartdecision2002@yahoo.com
www.slideshare.net/wings_of_wisdom