<abstract xmlns="http://www.w3.org/1999/xhtml"><p>In recent years, a lot of effort has been put into finding suitable mathematical models that fit historical data set. Such models often include coefficients and the accuracy of data approximation depends on them. So the goal is to choose the unknown coefficients to achieve the best possible approximation of data by the corresponding solution of the model. One of the standard methods for coefficient estimation is the least square method. This can provide us data approximation but it can also serve as a starting method for further minimizations such as Matlab function fminsearch.</p></abstract>

In recent years, a lot of effort has been put into finding suitable mathematical models that fit historical data set. Such models often include coefficients and the accuracy of data approximation depends on them. So the goal is to choose the unknown coefficients to achieve the best possible approximation of data by the corresponding solution of the model. One of the standard methods for coefficient estimation is the least square method. This can provide us data approximation but it can also serve as a starting method for further minimizations such as Matlab function fminsearch.

Data approximation using Lotka-Volterra models and a software minimization function

Department of Mathematical Analysis and Numerical Mathematics, Faculty of Mathematics, Physics and Informatics

Mathematical Institute of Slovak Academy of Sciences

Journal of Applied Mathematics, Statistics and Informatics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

In recent years, a lot of effort has been put into finding suitable mathematical models that fit historical data set. Such models often include coefficients...

Data approximation using Lotka-Volterra models and a software minimization function

Pubblicato online: 21 dic 2019

Pagine: 5 - 14

Ricevuto: 01 feb 2019

DOI: https://doi.org/10.2478/jamsi-2019-0005

Parole chiave
Systems modelling, linear regression, error minimization, integral method

© 2019 Faculty of Natural Sciences, University of Saint Cyril and Metodius

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Data approximation using Lotka-Volterra models and a software minimization function

Pubblicato online: 21 dic 2019

Pagine: 5 - 14

Ricevuto: 01 feb 2019

DOI: https://doi.org/10.2478/jamsi-2019-0005

Parole chiaveSystems modelling, linear regression, error minimization, integral method

© 2019 Faculty of Natural Sciences, University of Saint Cyril and Metodius

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Parole chiave
Systems modelling, linear regression, error minimization, integral method