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)