Ensemble of Differential Equations using Pareto Optimal for Traffic Forecasting

Bin Yang, Yuehui Chen, Mingyan Jiang

Abstract


The formal and empirical proof is that the ensemble of the learning models performs better than the single one. In order to construct the ensemble of the system of ordinary differential equations (ODEs), the two problems (diversity and accuracy of ODEs) are considered. In the paper, we estimate experimentally the model ensemble using multi-objective optimization. This paper presents a pareto optimal approach for identifying a family of the additive tree models which are used to reconstruct and identify the system of ordinary differential equations to predict the small-time scale traffic measurements data. We employ the tree-structure based evolution algorithm and particle swarm optimization (PSO) to evolve the architecture and the parameters of the additive tree model. The small-scale traffic measurements data is used to test ODE ensemble, and experimental results reveal that the proposed method is feasible and efficient for forecasting the time series.

 

DOI: http://dx.doi.org/10.11591/telkomnika.v11i12.2878


Keywords


multiobjective optimization; pareto optimal approach; the additive tree models; ordinary differential equations; small-scale traffic

Full Text:

PDF

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Indonesian Journal of Electrical Engineering and Computer Science (IJEECS)
p-ISSN: 2502-4752, e-ISSN: 2502-4760
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

shopify stats IJEECS visitor statistics