Attractor-based models for individual and groups' forecasting

5th International Workshop on Mathematical Models and their Applications 2016

Astakhova, N.N.^1 ; Demidova, L.A.^1,2 ; Kuzovnikov, A.V.^3 ; Tishkin, R.V.^1

Ryazan State Radio Engineering University, Gagarin Str. 59/1, Ryazan

390005, Russia^1

Moscow Technological Institute, Leninskiy pr. 38A, Moscow

119334, Russia^2

JSC Academician M.F. Reshetnev Information Satellite Systems, Lenin Street 52, Zheleznogorsk, Krasnoyarsk region

662972, Russia^3

关键词: Clonal selection algorithms;    Forecasting error;    Forecasting models;    Long time series;    Quality indicators;    Short time series;    Strictly binary trees;    Training data;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/173/1/012003/pdf
DOI  :  10.1088/1757-899X/173/1/012003

来源: IOP

【 摘 要 】

In this paper the questions of the attractors' application in case of the development of the forecasting models on the base of the strictly binary trees have been considered. Usually, these models use the short time series as the training data sequence. The application of the principles of the attractors' forming on the base of the long time series will allow creating the training data sequence more reasonably. The offered approach to creation of the training data sequence for the forecasting models on the base of the strictly binary trees was applied for the individual and groups' forecasting of time series. At the same time the problems of one-objective and multiobjective optimization on the base of the modified clonal selection algorithm have been considered. The reviewed examples confirm the efficiency of the attractors' application in sense of minimization of the used quality indicators of the forecasting models, and also the forecasting errors on 1 - 5 steps forward. Besides, the minimization of time expenditures for the development of the forecasting models is provided.