The JOB
Almost DONE
All Australian Airports
How To Achieve less than 1 % Error
The Job Almost Done
To Achieve less than 1 % Error
By Mohammed Salem Awad
consultant
When I started my carriers I faced ma...
Forecasting
Accuracy Matrix:
One of the new creative methodology. It
basically developed based on two main
estimated mathe...
Data Base ( 2010 – 2012 )
First Analysis:
1- Forecasting based R square Value ( Classical Method )
2- Forecasting based On...
Second Analysis:
2- Forecasting based On Setting Signal Tracking to Zero
Ased And how to
Forecasting:
Third Analysis:
3- Forecasting based On Setting Signal Tracking
in accepted region ( -4 < S.T. < 4 ) ( Max/Min S. T. appro...
The Results
( Third Analysis):
The accuracy of forecasting in third one is almost fair, yes the signals tracking are in th...
Get Your Own Targets of ( 2013- 2014)
By Filling the Forms : The first 50 requests are Free !!!!!!!!
To: smartdecision2002...
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The job almost done

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The Job almost Done, it is a forecasting study concerning some airports using signal tracking approach and coefficient of Determination. Yes the result is outstanding just take a look!!!

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  • Just to keep monitoring for the results and to see how far we can get results that less than 1%
    The BITRE web site is publishing updated traffic figure for May 2013 ( Actual ) which is 11,292,301 while the forecasted figure is 11,370,447 - the errors is also than 1 % i.e ( -0.69 )
    details for the data source in the link of http://www.bitre.gov.au/publications/ongoing/airport_traffic_data.aspx

    Release Date: August 2013
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The job almost done

  1. 1. The JOB Almost DONE All Australian Airports How To Achieve less than 1 % Error
  2. 2. The Job Almost Done To Achieve less than 1 % Error By Mohammed Salem Awad consultant When I started my carriers I faced many challenges, challenge in implementing theory in practice, one of these were setting goals and targets, which basically relay on forecasting, at that time I worked hard to develop a seasonality model especially for airline, it take about six month to solve such seasonality model using AREMA model with the final years student of Aden University, and a paper about that work was published in 1998 - Warangal conference – India, Yes it was hard and touch paper we can refer to the following link more details http://www.slideshare.net/fullscreen/wings_of_wisdom/a- multiplicative-time-series-model/1 . The path of that trails followed by a new study concerning US carriers – developing what so called Fair – Poor Accuracy Matrix defining a new performance factor as Signal Tracking and it conjugate impact with coefficient of determination R to align the best results of line fitting for seasonality model, and that can evaluate by addressing the displacement and directional factors of the mathematical model which is also define 4 regions to hold the right decision as Fair, Mislead, Unrelated and poor. A detail work we find it in a paper concerning Major US Airports in the following link http://www.slideshare.net/fullscreen/Aviation_Articles/accuracy-of-forecasting-model-us-carriers/1 Goals and Targets: Top managements always ask about achieving goals and targets, but at what level, and what is our objectives, is it short term targets or long term targets, how we interpolate the results, is it logic to accept the results or just to implement the formula. Really all this inquires lead us to practice the term “Forecasting By Objective” Based on what the analysis proposed there are many methods as 1- Forecasting based R square Value ( Best Value ) 2- Forecasting based On Setting Signal Tracking to Zero 3- Forecasting based On Setting Signal Tracking in accepted region ( -4 < S.T. < 4 ) 4- Forecasting based on most recent year 5- Forecasting to meet a specified target ( Trend Target ). We will address the first 3 methods and we will compare the results
  3. 3. Forecasting Accuracy Matrix: One of the new creative methodology. It basically developed based on two main estimated mathematical parameters, Displacement and Directional factors which has a consequence impacts on R and Signal Tracking by setting boundary accuracy: For Fair forecasting, the model should fulfill these criteria – (Golden Rules) R2 ≥ 80 and Signal Tracking should be - 4 ≤ S. T. ≤ + 4 Then by developed Forecasting Accuracy Matrix the following outcomes will be concluded 1- Fair Forecast – when R2 and Signal Tracking are in bond. 2- Mislead – Displacement Issue. This case when R2 is in bond and Signal Tracking is out bond. we can adjusted signal tracking to be in bond when there is a room for R2 in the same analysis so that it can be consider as a fair forecast. 3- Unrelated – Directional Issue. This case when R2 is out of the bond and Signal Tracking in the bond. i.e the balance of accumulated error without any correlation 4- Poor Forecast – when both R2 and Signal Tracking are out of the bond ( Total Mess). This matrix manipulate the four decision regions to develop the right and best picture of the accuracy of forecasting. And to enhance the process of decision making for airline data analysis especially traffic forecasting, that maps the overall forecasting accuracy of Airports.
  4. 4. Data Base ( 2010 – 2012 ) First Analysis: 1- Forecasting based R square Value ( Classical Method ) 2- Forecasting based On Setting Signal Tracking to Zero 3- Forecasting based On Setting Signal Tracking in accepted region ( -4 < S.T. < 4 ) 4- Forecasting based on most recent year
  5. 5. Second Analysis: 2- Forecasting based On Setting Signal Tracking to Zero Ased And how to Forecasting:
  6. 6. Third Analysis: 3- Forecasting based On Setting Signal Tracking in accepted region ( -4 < S.T. < 4 ) ( Max/Min S. T. approach )
  7. 7. The Results ( Third Analysis): The accuracy of forecasting in third one is almost fair, yes the signals tracking are in the bond and the coefficient of Determination is high ( 93.6 % ). While for comparison propose, we evaluate the errors of 2013 ( 4 months actual data with the forecast ), the results listed below with errors less than one. The four months of 2013 comparing result shows that the Error is less than 1 % !
  8. 8. Get Your Own Targets of ( 2013- 2014) By Filling the Forms : The first 50 requests are Free !!!!!!!! To: smartdecision2002@yahoo.com ( Mohammed S. Awad ) (Monthly - Form) Months 2010 2011 2012 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec (Annually - Form) Years Data 2005 2006 2007 2008 2009 2010 2011 2012

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