In this article, three approaches for detecting outliers, breakpoints and/or level shifts in time series are discussed. These approaches are then applied in a short financial time series with the generic aim to estimate the possible opportunity loss due to the occurrence of these points....
A unifying framework for detecting outliers and change points from time series We are concerned with the issue of detecting outliers and change points from time series. In the area of data mining, there have been increased interest in... J Takeuchi,K Yamanishi - 《IEEE Transactions on ...
However, Delaunay triangulation is more computationally expensive and more sensitive to outliers than KNN. Future studies could explore whether the computational cost of using Delaunay triangulation for imaging-based SRT data is justified, and how this decision may vary depending on the tissue structure...
Furthermore, outliers detection techniques are evaluated. Specifically, One-class SVM is tested in the same scenario. In this case, obtained results are slightly better than those obtained with the Manhattan distance, but they do not reach the keyboard dynamics levels (see Table 7). Thus, the ...
and Freeman, P., "Care and handling of univariate outliers in the general linear model to detect spuriosity - a Bayesian approach," Technometrics 20, 1978, 187-194.Guttman, I. Dutter, R. Freeman, P.R. (1978). Care and handling of uni- variate outliers in the general linear model to...
[~,groundIndices,outliers] = pcfitplane(ptCloudIn,maxGroundDist,referenceVector,maxAngularDist); ptCloudOut = select(ptCloudIn,outliers); obstacleIndices = currentIndices(outliers); groundIndices = currentIndices(groundIndices); end function [ptCloudOut,indices,croppedIndices] = cropPointCloud(ptClo...
Schölkopf B, Williamson R, Smola A et al (1999) Support vector method for novelty detection, pp 582–588 Breunig MM, Kriegel H-P, Ng RT, Sander J. LOF Identifying density-based local outliers, p 12 Starczewski A, Goetzen P, Er MJ (2020) A new method for automatic determining of...
The latter model requires to select the kernel function in Eq. (3) and its associated parameters according to the processing presented in the next section. The selected model is then used to identify the presence or absence of debondings (outliers) over the unknown pavement area. The learning...
Fig. 2. a) Box and whisker plot showing the ratio of C18:1 n-9: n-7 in avocado oil compared to other potential adulterant oils where black dots indicate outliers in each dataset. HO = high oleic; b) Box and whisker plot of the same data seen in (a) but log transformed to bett...
DBOD is an efficient method to find outliers in large databases. A value v is an outlier in DBOD if the distance to its closest neighbor v ′ is over some threshold D, i.e., minv′ v dist(v, v ′) > D. We use the same pattern distance as in SVDD, and rank the outliers ...