Predicting the Seasonal Performance of Market Segments for Airline Industry - 2019

1. Predicting the Seasonal Performance of
Market Segments for Airline Industry - 2019
Introduction
The word predict always accompanied with setting
targets i.e dealing with real numbers – and in that sense,
we prefer to set targets at lower acceptable level to
avoid a high risk of not achieving the desirable setting
goals. Also we don’t accept a negative trends to be a
desirable targets. (Never set a negative target)
Conversely, in case of predicting performance in terms
of load factor (L/F), the trend performance line can be
upward, leveling or downward. Because we interesting
to know the behavior of the trend (Performance) not to
set target, so accordingly the monthly model selection
process will be based on the higher value of R – square
and not on the lower level of Risks (Target).
Therefore:
In short term forecasting, two objectives we address,
First: Annual Forecasting – which defining the
trend (Positive or Negative).
Second: Monthly Forecasting – which defining the
seasonality (Optimum Solution without constrains),
Also
Twelve Moving Average is a powerful tool to damp the
discrepancy level of the data.
A complete approach of setting target for RPKs and ASKs can demonstrated by the following link
https://www.slideshare.net/wings_of_wisdom/predicting-aviation-industry-performance-lf-2019
While to predict the performance can be, explore by following article.
First : Annual Forecasting – which defining the trend (Positive or Negative).
The best way to set up annual target and minimize the data discrepancy is to address the data by two
trend models using the concept of 12 rolling months.
First – General Trend Model using the concept of Straight Line equation – defining general trend.
Second – Most Recent Data Trend Model Using a Polynomial Model – Second-degree equation.
This reflects the impact of most recent data on the path of general trend. The mid-point is the most
convenient forecast annual result at Dec 2019. So as long as the gap between two models is small, the
more accurate approaching value for setting annual target otherwise we have to select the half way
distance between two extreme targets of these two models provided that Dec 2019 > Dec 2018.
However, the outcomes can be represents by three directions
1- Positive Trends – both models in Upwards direction. The
selection will be either the mid-point of positive trends Dec
2019 (Red Column) or the value of optimum solution (blue
column) – select the one who has higher R-square value.
By: Mohammed Salem Awad
Aviation Consultant
Data Source:
https://www.iata.org/publications/economics/Pages/index.aspx

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2- Negative Trends – both models in downwards direction –
However, here the story is different (as we are interesting
in predicting performance, either good or bad, or positive
or negative, or upward or downward trend. We select the
one that has higher value of R-square.
3- High Discrepancy Data – this is occur when we apply
two trend models, one has a positive trend, and the other
one has a negative trend. i.e mean large gap between the
two models, as we mentioned above we look to the one
who has a higher R-square.
Second: Monthly Forecasting – which defining the seasonality
(Optimum Solution without constrains)
This is the main core program to define the seasonality
pattern without any preset constrains, it follows the trends
based on 36 months database. Which always comes closely to
the preset values. Moreover, the blue column in the annual
graph represents it. Therefore, we get a complete picture to select the right performance
level. However, when it’s a higher value, then we have to select the preset values as a best
performance level.
Input Data :Air Passenger Market Analysis – Report
In depth analysis that addressed by Iata Economsit. We can
use these data for our analysis to predict the performance
of all Market Areas in the world.
The World Market Segments according to IATA
Specification are:
1- Total Airline Industry
a. International
b. Domestic
Also
1- Total Airline Industry
a. Total Africa Market.
b. Total Asia Pecific Market.
c. Total Europe Market
d. Total Latin America Market
e. Total Middle East Market
f. Total North America Market

3. 33
Analysis:
All Total Airline Industry and its market segment as African Market, Asia Pecific Market,
Europe Market, Latin America Market, Middle East Market and North America Market are
analysed annually and monthly.
1- Total Airline Industry Market:
- 2019 Perdicting Perforamce (Load Factor) = 82.21 %
- Value of R-square : 90.03 %
- Data Discrepancy: Medium

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2- Total Africa Market:
- 2019 Perdicting Perforamce (Load Factor) = 72.71 %
- Value of R-square : 87.86 %
- Data Discrepancy: Medium

5. 55
3- Total Asia Pecific Market :
- 2019 Perdicting Perforamce (Load Factor) = 83.00 %
- Value of R-square : 77.16 %
- Data Discrepancy: High

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4- Total Europe Market:
- 2019 Perdicting Perforamce (Load Factor) = 85.48 %
- Value of R-square : 94.91 %
- Data Discrepancy: Low

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5- Total Latin America Market
- 2019 Predicting Perforamce (Load Factor) = 81.96 %
- Value of R-square : 85.81 %
- Data Discrepancy : High

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6- Total Middle East Market
- 2019 Perdicting Perforamce (Load Factor) = 73.25 %
- Value of R-square : 85.81 %
- Data Discrepancy: Medium

9. 99
7- Total North America Market
- 2019 Perdicting Perforamce (Load Factor) = 83.97 %
- Value of R-square : 91.55 %
- Data Discrepancy: Low

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Results
L/Factor R-square Data
% % Discrepancy
1- Total Airline Industry 82.21 90.03 Medium
a. Tot. Africa Market. 72.71 87.86 Medium
b. Tot. Asia Pecific Market. 83.00 77.16 High
c. Tot. Europe Market 85.48 94.91 Low
d. Tot. Latin America Market 81.96 70.36 High
e. Tot. Middle East Market 73.25 85.81 Medium
f. Tot. North America Market 83.97 91.55 Low
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