Consistency controlled future generating models
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Pitlik, Pető , Pásztor, Popovics, Bunkóczi , Szűcs University Gödöllő, Hungary PowerPoint PPT Presentation


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Consistency controlled future generating models Mapping Time and Space for checking environmental consistencies with COCO methodology. Pitlik, Pető , Pásztor, Popovics, Bunkóczi , Szűcs University Gödöllő, Hungary. Introduction.

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Pitlik, Pető , Pásztor, Popovics, Bunkóczi , Szűcs University Gödöllő, Hungary

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Consistency controlled future generating models Mapping Time and Space for checking environmental consistencies with COCO methodology

Pitlik, Pető , Pásztor, Popovics, Bunkóczi, Szűcs

University Gödöllő, Hungary

Supported by the Hungarian Research Found (OTKA T049013)


Introduction

  • Model fitting is not always ensured with enough care: error min. ≠ model consistency.

  • The processes (objects) in scope of each modelling are happening in Space and in Time.

  • Hypothesis: the built up models authenticity may be examined by the consistency of the attributes of the time and/or space connected objects.

  • Methodological background – Similarity analysis: COCO methodology (Component based Object Consistency for Objectivity)

Supported by the Hungarian Research Found (OTKA T049013)


COCO methodology I.

  • comparing n objects on the base of their m common attributes (as inputs) and one output (e.g. price or time, space)

  • ranking on the base of the attributes – specifying the rank values for each starting value for each attributes

  • Ordering (through Excel Solver) „COCO-parameter” for each rank value

Supported by the Hungarian Research Found (OTKA T049013)


COCO methodology II.

  • Constraint in case of all attribute, that the below COCO value may get only lower or equal value than the upper one

  • Collecting COCO values for each objects

  • Connecting the COCO values with arbitrary function and giving a „summarising” value to each object as an estimation

  • Moving these COCO values by Excel Solver to get their direct or transformed values to the most close to the real Y values.

  • Difference minimisation principles: e.g. quadratic error (for price analysis) or Pearson coefficient (for modelling of space and time).

Supported by the Hungarian Research Found (OTKA T049013)


Introducing the topics

  • Time: Describing dynamic (in time happening) phenomena with it`s characteristic attributes – possibility of intra- and extrapolation – the Time is the Y (case study: meteorology)

  • Space: Describing the situations of objects located in space with their characteristic attributes - the direction in flat is the Y (case study with random values)

Supported by the Hungarian Research Found (OTKA T049013)


The time

  • Describing the time in general

    • http://miau.gau.hu/miau/82/time-space_coco.xls

  • Describing the time according to meteorological attributes

    • http://miau.gau.hu/miau/83/coco_meteorologia.xls

Supported by the Hungarian Research Found (OTKA T049013)


Supported by the Hungarian Research Found (OTKA T049013)


Supported by the Hungarian Research Found (OTKA T049013)


Supported by the Hungarian Research Found (OTKA T049013)


Supported by the Hungarian Research Found (OTKA T049013)


Supported by the Hungarian Research Found (OTKA T049013)


The space

  • Describing the space in general

    • http://miau.gau.hu/miau/82/time-space_coco.xls

  • Possible test (…coming soon…): examination of the data involved in METAR telegrams for one given moment and for about 100km*100 km size area`s measure points

Supported by the Hungarian Research Found (OTKA T049013)


Supported by the Hungarian Research Found (OTKA T049013)


Supported by the Hungarian Research Found (OTKA T049013)


Supported by the Hungarian Research Found (OTKA T049013)


Supported by the Hungarian Research Found (OTKA T049013)


Supported by the Hungarian Research Found (OTKA T049013)


Inner values

  • The importance of attributes – the average of COCO values

  • The homogeneity of attributes – standard deviation of the COCO values

  • Ranking algorithm are responsible for the length of the potential forecasting

  • The error definition makes strong influence for the sum of errors

Supported by the Hungarian Research Found (OTKA T049013)


Application possibilities for COCO

  • Benchmarking - Ranking - Learning

    • Objective utility analysis (price/cost comparing, country/employee-ranking, automated SWOT analysis)

    • Forecasting (e.g. stock change of animals)

    • Checking authenticity of experts/models (checking possible status-variations of future = consistency)

Supported by the Hungarian Research Found (OTKA T049013)


Outlook

  • Further case studies are needed for testing the fine tuning and generic aspects of the method (e.g. optimum of forecasting potential, solving linearity originating from ranking)

  • Possible further applications for testing of the potential through this methodology:

    • examining meteorological data connected to different locations but to the same moment

    • model selection for forecasting by stock change of pigs

    • Your modelling approach?

Supported by the Hungarian Research Found (OTKA T049013)


The research is supplied by OTKA T-049013 …

For further details

Thanks for Your attention!

Supported by the Hungarian Research Found (OTKA T049013)


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