RollingSkew / Computation Layer

Calculate the rolling skewness (asymmetry measure) over a sliding window. Similar to pandas.rolling().skew() or R's roll_skew().

Mathematical form: $skew = \frac{E [( X - μ ) ^{3} ]}{σ ^{3}}$ where $μ$ is mean and $σ$ is standard deviation.

Common applications:

Financial returns analysis
Distribution asymmetry tracking
Risk assessment in markets
Quality control processes
Natural phenomena analysis
Portfolio optimization
Anomaly detection

Note: Positive skew indicates right tail; negative indicates left tail.

Table

Select

column

The numeric column to compute rolling skewness for. Common inputs:

Financial returns (market sentiment)
Error distributions (process control)
Performance metrics (distribution shape)
Natural measurements (asymmetric patterns)
Reaction times (response distributions)

WindowSize

u32

Number of observations in sliding window. Common periods:

21: Monthly trading skewness
60: Quarter-hour analysis for hourly data
90: Quarterly analysis

Larger windows provide more reliable skewness estimates.

Bias

bool

false

Statistical bias correction flag. When false, applies correction factor:

true: Maximum likelihood estimate
false: Unbiased estimate (n/(n-1)(n-2))

Unbiased estimation recommended for smaller windows.

Name for the new column. If not provided, the system generates a unique name. If AsColumn matches an existing column, the existing column is replaced. The name should follow valid column naming conventions.