An auto regression compression method for industrial real time data
Tiecheng Pu, Jing Bai · 2014
According to the continuity and monotonicity of industrial real time data, an auto regression compression method (for short ARCM) is proposed. Firstly, the auto regression model of a group of sampled sequence is established. Secondly, the next sampled data can be predicted by the model. If the error between the actual data and the predictive data is in the allowable range, we save the parameters of model and the beginning data. Otherwise, we save the data and repeat the method from the next sampled data. At Last, the method is applied to a beer production electricity data compression. The result verifies the effectiveness of proposed method.