Modifying the method for forecasting hazardous processes with unknown dynamics in the presence of noise

This paper has substantiated a modified method that, within the framework of the adaptive zero­or­der Brown’s model, provides for increased accuracy in predicting processes with unknown dynamics masked by the noise of various levels. The forecasting method modifica­tion essentially involves an adaptive technique for determining the weight of the correction of the previous fore­cast, taking into consideration the recurrent state of the predicted pro­cess in time. To investigate the accu­racy of the forecasting method, a test model of the process dynamics was determined in the form of a rectan­gular pulse with unit amplitude. In addition, a model of additive mask­ing noise was defined in the form of a discrete Gaussian process with a zero mean and a variable value of the mean square deviation. Based on determining the exponentially smoothed values of current absolute forecasting errors, the dynamics of forecast accuracy were examined for the modified and self­adjusting me­thods. It was found that for the mean quadratic deviation of the masking noise equal to 0.9, the smoothed abso­lute prediction error for the modi­fied method does not exceed 23%; for the self­adjusting method – 42%. This means that the prediction ac­curacy for the modified method is about twice as high. In the case of an average square deviation of mask­ing noise of 0.1, the smoothed abso­lute prediction error for the modi­fied and self­adjusting methods is approximately the same and does not exceed 10%. That means that at a low level of masking noise, both prediction methods provide approxi­mately the same accuracy. However, with an increase in the level of mask­ing noise, the self­adjusting method significantly loses the accuracy of the forecast to the proposed modi­fied method