Time-Series Prediction of Iron and Silicon Content in Aluminium Electrolysis Based on Machine Learning

Abstract

In analyzing dynamic characteristic of time-series data, classic prediction models rely heavily on static historical data, and tacit knowledge is difficult to be mined effectively. Therefore, a hybrid prediction model GS-GMDH is proposed based on growing neural gas (GNG) and the group method of data handling (GMDH). Firstly, a dynamic prediction mechanism, based on an incremental learning algorithm and time-series prediction, is established by GS-GMDH, by which the singularity is recognized and the prediction efficiency is improved. Secondly, to compare the performance of the proposed method, the multi-step ahead predictions with time-series data onto iron and silicon content are employed, and the new model is compared with classic machine models. Finally, the results show that the hybrid prediction model (GS-GMDH) proposed in this paper ensure an accurate and efficient prediction of time-series data for iron and silicon content.