LTR – MDTS structure – A structure for Multiple Dependent Time Series Prediction

Predrag Pecev1 and Miloš Racković2

  1. Unversity of Novi Sad. Technical faculty “Mihajlo Pupin”
    Đure Đakovića BB, 23000, Zrenjanin
    pecev@tfzr.uns.ac.rs
  2. Unversity of Novi Sad, Faculty of Sciences
    Trg D. Obradovića 3, 21000, Novi Sad
    rackovic@dmi.uns.ac.rs

Abstract

The subject of research presented in this paper is to model a neural network structure and appropriate training algorithm that is most suited for multiple dependent time series prediction / deduction. The basic idea is to take advantage of neural networks in solving the problem of prediction of synchronized basketball referees’ movement during a basketball action. Presentation of time series stemming from the aforementioned problem, by using traditional Multilayered Perceptron neural networks (MLP), leads to a sort of paradox of backward time lapse effect that certain input and hidden layers nodes have on output nodes that correspond to previous moments in time. This paper describes conducted research and analysis of different methods of overcoming the presented problem. Presented paper is essentially split into two parts. First part gives insight on efforts that are put into training set configuration on standard Multi Layered Perceptron back propagation neural networks, in order to decrease backwards time lapse effects that certain input and hidden layers nodes have on output nodes. Second part of paper focuses on the results that a new neural network structure called LTR-MDTS provides. Foundation of LTR-MDTS design relies on a foundation on standard MLP neural networks with certain, left-to-right synapse removal to eliminate aforementioned backwards time lapse effect on the output nodes.

Key words

MLP, Multiple Dependent Time Series, LTR-MDTS structure, Training parameter influence, Neural Network Configuration, Training Set Configuration and Optimization.

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS150815004P

Publication information

Volume 14, Issue 2 (June 2017)
Year of Publication: 2017
ISSN: 1820-0214 (Print) 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Pecev, P., Racković, M.: LTR – MDTS structure – A structure for Multiple Dependent Time Series Prediction. Computer Science and Information Systems, Vol. 14, No. 2, 467–490. (2017)