
In the article an effective method of analysis and forecasting of cyclic time series of dynamics illustrated by instrumental measurements of the surface atmospheric temperature from 1850 to 2009 is proposed. The method is based on the use of multiscale decomposition of a signal of the wavelet functions and adaptive hybrid neurofuzzy network inference rules.
В статье предложен эффективный метод анализа и прогнозирования циклических временных рядов динамики на примере инструментальных измерений приземной атмосфер-ной температуры с 1850 по 2009 годы. Метод основан на использовании кратномасштабного разложения сигнала с использованием вейвлет-преобразований и гибридной сети нейронечетких правил вывода.
ВРЕМЕННЫЕ РЯДЫ ДИНАМИКИ,ВЕЙВЛЕТ-ПРЕОБРАЗОВАНИЕ,КРАТНО-МАСШТАБНОЕ РАЗЛОЖЕНИЕ,ФАЗО-ЧАСТОТНАЯ ХАРАКТЕРИСТИКА,НЕЙРОННАЯ СЕТЬ,НЕЧЕТКИЙ ВЫВОД,ПРОГНОЗИРОВАНИЕ,TIME-SERIES OF DYNAMICS,WAVELET TRANSFORM,MULTIPLE-SCALE DECOMPOSITION,PHASE-FREQUENCY CHARACTERISTICS,NEURAL NETWORK,FUZZY CONCLUSION,FORECASTING
ВРЕМЕННЫЕ РЯДЫ ДИНАМИКИ,ВЕЙВЛЕТ-ПРЕОБРАЗОВАНИЕ,КРАТНО-МАСШТАБНОЕ РАЗЛОЖЕНИЕ,ФАЗО-ЧАСТОТНАЯ ХАРАКТЕРИСТИКА,НЕЙРОННАЯ СЕТЬ,НЕЧЕТКИЙ ВЫВОД,ПРОГНОЗИРОВАНИЕ,TIME-SERIES OF DYNAMICS,WAVELET TRANSFORM,MULTIPLE-SCALE DECOMPOSITION,PHASE-FREQUENCY CHARACTERISTICS,NEURAL NETWORK,FUZZY CONCLUSION,FORECASTING
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