Time series prediction using the adaptive resonance theory algorithm ART-2

Abstract

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.

A review of recent advances and applications of machine learning in tribology

“…ART-2 model not only solves the problem of pattern and situation recognition but also can be used to study and predict the structure and values of time series. 212 An ART-2 model was employed by Subrahmanyam and Sujatha, which was further compared with conventional multilayered feed-forward ANN with error backpropagation (EBP). 213 These ML models were trained for the diagnosis/detection of localized defects in ball bearings.…”