Prediction of Multivariate Time-Series Based on the Smart Interpolation with Chebyshev Polynomials (Tspredict)

Project no.: PP59/2011

Project description:

Novel techniques and algorithms for the segmentation and prediction of the univariate and multivariate time-series will be constructed during the project. Mathematical model based on the interpolation of the univariate time-series with Chebyshev polynomials within a non-uniform time-grid will be proposed. Such a model estimates the fact that values of the time-series near the present time moment have more influence on the future value than older ones. The model builds the ground for the algorithm of the prediction of signals with a high noise level. The implementation of such an algorithm runs into the ill-conditioned optimization problem which requires a construction of novel non-standard cost functions optimized employing adaptive evolutionary optimization algorithms. Moreover, novel segmentation techniques and algorithms, which enable to improve the quality of the prediction of the isolated scalar time-series, employing reconstructed near optimal mathematical models of the related time-series, will be proposed.

Project funding:

KTU Science and Innovation Fund

Period of project implementation: 2020-04-14 - 2020-12-31

Project coordinator: Kaunas University of Technology

Loreta Saunorienė

2020 - 2020

Department of Mathematical Modelling, Faculty of Mathematics and Natural Sciences

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