New method for outlier removal from observed data based on fuzzy forecasting system
Yang Ruo-hong · Systems engineering and electronics · 2006
To determine the outliers having an influence on data processing and analysis in observed data,a new on-line method for identifing and eliminating outliers based on fuzzy forecasting system of time series.Firstly,the optimal fuzzy forecasting system is designed according to the least mean square criterion by gradient descent algorithm,and residual error sequence between observed data and predicted data is obtained.Then outliers are identified and discarded according to exceptional value in residual error sequence based on Dickson criterion.Effectiveness of the new method is proved through experiments with real observed data.The on-line fuzzy forecasting system of time series can exactly track the signals,suitable to identify and discard outliers in various of signals with right initialization.