Statistical properties of signal entropy for use in detecting changes in time series data
Why this work is in the frame
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Bibliographic record
Abstract
Detecting changes in an underlying time series model for a system is an important task in many different fields, including econometrics, geophysics and process control. Specifically, in process control, detecting model changes is often the first step for fault detection, plant‐model mismatch assessment and data quality assessment for system identification. Signal entropy, which basically measures the amount of disorder in a given signal, can, not only segment a time series, but can also determine which regions have similar underlying models. Thus, the changes between the input and output signals can be used to determine when model is no longer an accurate representation of the system by comparing the current differential entropy against the historical differential entropy. This paper presents the statistical properties of signal entropy for discrete time systems. An example of the general results is provided by determining the entropy characteristics for first‐order systems driven by white noise. As well, a change detection index is proposed to assess changes in the time series model, which is tested on an experimental system. Copyright © 2013 John Wiley & Sons, Ltd.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it