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Since these time series are more often non linear, complex and massive, therefore the applied predictor method should be able to detect the discord patterns from a large data in a short time.
High order spatial accuracy is obtained through a WENO reconstruction, while a high order one-step time discretization is achieved using a local space time discontinuous Galerkin predictor method.
High order time discretization is achieved via a one-step ADER approach that uses a local space time discontinuous Galerkin predictor method to evolve the data locally in time within each cell.
The resulting first-order accurate centered method is then extended to high order of accuracy in space via a high order WENO reconstruction technique and in time via a local continuous space time Galerkin predictor method.
Here, we use again an element-local space time Galerkin finite element predictor method to achieve a high order accurate one-step time discretization, while the somewhat expensive WENO approach on moving meshes, used to obtain high order of accuracy in space, is replaced by an a posteriori MOOD loop which is shown to be less expensive but still as accurate.
We found out that the classification accuracy ranged from 89% (compound covariate predictor method) to 99% (SVM), and confirmed the best performance of SVM-based methods to analyze these data (Table 3).
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In this study, we compared prediction accuracy of three different state-of-the-art predictor methods.
Its performance is compared to other previously published predictor methods (Moal et al., 2011).
The 438 GIs detected are shown in Additional file 2 including predictor methods used to detect them and some features such as the presence of MGE like plasmids, transposases, integrons, conjugative transposons or phages.
These methods are such as the predictor corrector method [11], Adomian decomposition method (ADM) [12 15], variational iteration method (VIM) [16, 17] and homotopy analysis method (HAM) [18, 19].
Compared to the random predictor, our method achieved much better performance.
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