Moreover, in some cases, outliers can give you information about localized anomalies in the whole system; so detecting outliers becomes a valuable step that enhances your understanding of the dataset. How to detect outliers: four detection techniques There are many techniques to detect and optionally...
Granular box regression utilizes intervals a challenge is the detection of outliers. In this paper, we propose borderline method and residual method to detect outliers in granular box regression. We also apply these methods to artificial as well as to real data of motor insurance.C.Prabu...
Schweder T (1976) Some “optimal” methods to detect structural shift or outliers in regression. JASA 71:491–501Some “optimal” methods to detect structural shift or outliers in regression - Schweder - 1976Schweder, T., 1976. Some ``optimal'' methods to detect structural shift or outliers ...
Moving Average(MA) is a commonly used means for analyzing time series. It can filter high-frequency noise and detect outliers. Based on different computation methods, common MA algorithms include simple moving average, weighted moving average, and exponential moving average. Assume that the...
Improving the quality of data and interpretation of phenomena: by removing outliers, we can focus more on important data patterns and relationships. To pre-process the data and remove outliers, spacing methods such as Z-score, modified Z-score, standard deviation, Tukey, adjusted boxplot, median...
WIRELESS NETWORKS: A COMPARISON AND CLASSIFICATION BASED ON OUTLIER DETECTION METHODSOutlier detection has been used for centuries to detect and, where appropriate, remove anomalous observations from data. Outliers arise due to mechanical faults, changes in system behavior, fraudulent behavior, human ...
The points A=(-0.5,-1.5) and B=(0.5,0.5) are outliers. Point A is outside the range defined by the y data, while Point B is inside that range. As we will see, that makes them of different nature, and we will need different methods to detect and treat them. ...
In this paper PCA (Principal Components Analysis) was utilized as unsupervised technique to detect multivariate outliers on the dataset of an hour duration of time. PCA is sensitive to outliers since it depend on non-robust estimators. This lead us using MCD (Minimum Covariance Determinant) and ...
Each test was made on a dataset of 1180 registers in which outliers have been introduced deliberately. The experimental results show that the method is able to detect all introduced values, which were previously labeled to be differentiated. Consequently, there were found a total of 48 tuples ...
Latent structure-based methods allow monitoring a large number of process variables, reducing the dimensionality of the original space to a few latent variables and statistics that are easy to control using charts. These methods are able to detect not only univariate outliers (i.e. values outside...