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Temporal Analysis

Temporal analysis tracks how a source changes across the stream.

Implemented in openentropy_core::temporal.

What it is: A time-structure analysis for drift, shifts, bursts, and anomalies.

Use it for: Diagnosing instability over time that aggregate metrics can hide.

Input shape: Usually one byte stream; stability checks use multiple session streams.

Use this when

Section titled “Use this when”
  • You suspect drift, bursts, or regime shifts over time.
  • Forensic metrics look unstable across repeated runs.
  • You need change-point or anomaly windows, not just aggregate scores.

Methods

Section titled “Methods”
  • change_point_detection / _default: significant mean shifts between adjacent segments
  • anomaly_detection / _default: outlier windows and anomaly rate
  • burst_detection / _default: high-intensity burst intervals
  • shift_detection / _default: windowed mean shifts with z-score threshold
  • temporal_drift / _default: trend slope and drift confidence over segments
  • inter_session_stability: cross-session consistency score
  • temporal_analysis_suite: one-call orchestrator for single-stream temporal checks

CLI

Section titled “CLI”
Terminal window
openentropy analyze --temporal
openentropy analyze --profile deep

Python SDK

Section titled “Python SDK”
from openentropy import temporal_analysis_suite, temporal_drift, inter_session_stability


suite = temporal_analysis_suite(data)
drift = temporal_drift(data)
stability = inter_session_stability([data_a, data_b, data_c])

Rust SDK

Section titled “Rust SDK”
use openentropy_core::{temporal_analysis_suite, temporal_drift, inter_session_stability};


let suite = temporal_analysis_suite(&data);
let drift = temporal_drift(&data, 10);
let stability = inter_session_stability(&[&data_a, &data_b, &data_c]);
Section titled “Related”