Non-Causal Step

Single Steps

• Computing Mean Value with Mean Algorithm
• Computing Median Value with Median Algorithm
• Computing Midrange Value with Midrange Algorithm
• Computing Mode Value with Mode Algorithm
• Computing Standard Deviation with Standard Deviation Algorithm
• Computing Trimmed-Mean Value with Trimmed Mean Algorithm
• Computing Weighted-Mean Value with Weighted Mean Algorithm
• Computing the First Order Derivative with Forward Difference
• Data Denoising with Centered Moving Average
• Data Denoising with Centered Moving Median
• Data Denoising with Fast Fourier Filter
• Data Denoising with Forward Moving Average
• Data Denoising with Forward Moving Median
• Data Denoising with LOWESS (Locally Weighted Scatterplot Smoothing)
• Data Denoising with Percentile Filter
• Data Denoising with RLOWESS (Robust Locally Weighted Scatterplot Smoothing)
• Data Denoising with Savitzky-Golay Smoothing
• Data Denoising with Weighted Centered Moving Average
• Data Denoising with Weighted Forward Moving Average
• Data Discretization with CAIM (Class-attribute Interdependence Maximization)
• Data Discretization with Equal-width Interval Binning
• Data Discretization with K-Means Clustering
• Data Discretization with One-Rule Discretizer (1RD)
• Data Reduction With Equal-width Interval Binning
• Data Reduction With Linear Regression
• Data Reduction With Polynomial Regression
• Data Reduction with K-Means Clustering
• Data Reduction with Principal Component Analysis (PCA)
• Data Reduction with Simple Random Sample with Replacement (SRSWR)
• Data Reduction with Simple Random Sample without Replacement (SRSWOR)
• Data Scaling with Decimal Scaling
• Data Scaling with Min-Max Scaling
• Data Scaling with Unitnorm Scaling
• Data Scaling with Zero-Mean Scaling
• Data Sorting with Bubble Sort
• Data Sorting with Quick Sort
• Data Sorting with Selection Sort
• Outlier Detection with Box Plot
• Outlier Detection with Dixon-type (Q) tests
• Outlier Detection with Grubbs’ Test
• Outlier Detection with Hampel Identifier
• Outlier Detection with Maximum Likelihood
• Outlier Detection with Tietjen-Moore Test
• Outlier Detection with Trimmed Mean
• Outlier Detection with Two-sided Median
• Reconstructing Improper Values with C4.5
• Reconstructing Improper Values with Linear Regression
• Reconstructing Improper Values with Local Centered Moving Average
• Reconstructing Improper Values with Local Centered Moving Median
• Reconstructing Improper Values with Local Centered Moving Midrange
• Reconstructing Improper Values with Local Centered Moving Mode
• Reconstructing Improper Values with Local Centered Moving Trimmed Mean
• Reconstructing Improper Values with Local Centered Moving Weighted Mean
• Reconstructing Improper Values with Polynomial Regression
• Reducing Data Nonnormality with Box-Cox Transformation
• Resolving Data Value Conflict with All Possibilities
• Resolving Data Value Conflict with Mean
• Resolving Data Value Conflict with Mode
• Stabilizing Variance with Box-Cox Transformation
• Testing Data Stationarity with Runs Test
• Testing Data Stationarity with Sign Test
• Variance Estimation with Mean Absolute Deviation (MAD)