Time series data is widely used in various fields, such as finance, economics, and engineering. One of the key challenges when working with time series data is detecting level shifts. A level shift occurs when the time series’ mean and/or variance changes abruptly. These shifts can significantly impact the analysis and forecasting of the time series and must be detected and handled properly.
Improving the detection of level shifts using the median filter
Efficient and automated bandwidth selection in SAS® PROC KPCA
The advantage of using SAS PROC KPCA is that you can preprocess your data so that you can classify groups with nonlinear classification boundaries.
SAS® Fast-KPCA: An efficient and innovative nonlinear principal components method
SAS® Fast-KPCA implementation bypasses the limitations of exact KPCA methods. SAS® internally uses k-means to find a representative sample of a subset of points. This row reduction method has the advantage that c centroids are chosen to minimize the variation of points nearest to each centroid and maximize the variation to the other cluster centroids. In some cases, the downstream effect of using k-means on computing the SVD increases numerical stability and improves clustering, discrimination, and classification.