Abstract
Prediction of gas emission has received much attention on the security of production of coal mining during the last decade. Gas emission is a random time series during the coal mining and the abnormal values can cause some dangerous events. We analyze these time series data and develop an online LS-SVR algorithm named TOLS-SVR based on time series data. Time series data from gas emission are converted into a set of vectors using theory of time series. The optimal embedding dimension is determined through cross validation technique. Experimental results demonstrate the good performance and efficiency of the proposed TOLS-SVR compared with the traditional online LS-SVR for gas emission prediction.
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