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Bo Mi Lim ․Cheong-Sool Park ․Jun Seok Kim․Sung-Shick Kim ․Jun-Geol BaekSchool of Industrial Management Engineering, Korea UniversityWe propose a method for estimating coefficients of AR (autoregressive) model which named MLPAR (Maximum Likelihood of Pearson system for Auto-Regressive model). In the present method for estimating coefficients of AR model, there is an assumption that residual or error term of the model follows the normal distribution. In common cases, we can observe that the error of AR model does not follow the normal distribution. So the normal assumption will cause decreasing prediction accuracy of AR model. In the paper, we propose the MLPAR which does not assume the normal distribution of error term. The MLPAR estimates coefficients of auto-regressive model and distribution moments of residual by using pearson distribution system and maximum likelihood estimation. Comparing proposed method to auto-regressive model, results are shown to verify improved perfor-mance of the MLPAR in terms of prediction accuracy.

A Study of Estimation Method for Auto-Regressive Model with Non-Normal Error and Its Prediction Accuracy