Algorithm For Concept Drift Detection In Autonomic Smart Buildings
Bibliographic record
Abstract
In order to support the self-awareness, self-configuration, self-description, and self-optimization autonomic properties, autonomic systems need to be able to detect concept drift in their external environment and in their internal operating characteristics. For some autonomic systems, such as smart buildings, this is a very difficult problem because their state is described by multivariate time-series data containing thousands of features generated by thousands of building sensors. Data generated by each sensor typically contains trend, seasonality, and cycles, as well as a significant amount of noise. In this paper we present a new statistical ensemble algorithm for detecting changes in noisy multivariate time-series data. Our algorithm can detect concept drift with up to 100% accuracy, and important change points with up to 92% precision, and 8% false-positive rate. Our algorithm was observed to reduce required features up to 5.4x, reducing the required on-line computational effort.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".