Detecting and Revamping of X-Outliers in Time Series Database

Dataset with Outliers causes poor accuracy in future analysis of data mining tasks. To improve the performance of mining task, it is necessary to detect and revamp of outliers which are there in the dataset. Existing techniques like ARMA (Auto-Regressive Moving Average), ARIMA (Auto-Regressive Integrated Moving Average) and Multivariate Linear Gaussian state space model don't consider the periodicity for outlier detection. The above methods are used to find out only Y Outliers which are present in Y axis. These methods are not applicable to detect the time at which the peculiar data occurs (so called X-Outliers). This paper focuses different methods for detecting and revamping of X-Outliers that have abnormal data according to a known periodicity.

Provided by: International Journal of Computer Applications Topic: Data Management Date Added: Dec 2012 Format: PDF

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