Open AccessTime series prediction using the adaptive resonance theory algorithm ART-2
Author(s) -
А. В. Гаврилов,
Olga K. Alsova
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1333/3/032004
Subject(s) - adaptive resonance theory , series (stratigraphy) , cluster analysis , component (thermodynamics) , computer science , algorithm , artificial neural network , artificial intelligence , resonance (particle physics) , time series , machine learning , pattern recognition (psychology) , physics , paleontology , particle physics , biology , thermodynamics
The algorithm of the adaptive resonant theory ART-2 is based on the ideas of dynamic clustering and the unsupervised learning model. The classic application of the ART-2 algorithm is related to the solution of pattern recognition problems in the framework of the neural network approach. The article proposes a modification of the adaptive resonance theory ART-2 as applied to the solution of the time series (TS) prediction problem. A description of the TS forecasting algorithm based on ART-2, its properties and application features, as well as the results of a study of TS free electricity prices of the “day-ahead market” (DAM) in Russia is here. The obtained results allow us to conclude about the prospects of using ART-2 to study the structure and prediction of TS with a periodic (seasonal) component.