Struct TemporalAnalysis 

pub struct TemporalAnalysis {
    pub window_size: usize,
    pub anomaly_count_a: usize,
    pub anomaly_count_b: usize,
    pub max_z_a: f64,
    pub max_z_b: f64,
    pub top_anomalies_a: Vec<WindowAnomaly>,
    pub top_anomalies_b: Vec<WindowAnomaly>,
    pub windowed_entropy_a: Vec<f64>,
    pub windowed_entropy_b: Vec<f64>,
}

Sliding-window temporal anomaly detection using theoretical parameters.

Window means are compared against the theoretical mean (127.5) and theoretical standard deviation (sqrt(Var(U(0,255)) / window_size)) rather than empirical estimates. This avoids the circular-statistics pitfall where z-scores computed from empirical parameters always produce ~0.27% outliers.

Fields

window_size: usize

anomaly_count_a: usize

anomaly_count_b: usize

max_z_a: f64

max_z_b: f64

top_anomalies_a: Vec<WindowAnomaly>

top_anomalies_b: Vec<WindowAnomaly>

windowed_entropy_a: Vec<f64>

Per-window Shannon entropy. Capped at 1024 entries in each vec to keep JSON output bounded; for larger sessions the windows are sub-sampled.

windowed_entropy_b: Vec<f64>

Trait Implementations

impl Clone for TemporalAnalysis

fn clone(&self) -> TemporalAnalysis

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for TemporalAnalysis

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Serialize for TemporalAnalysis

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.