Neural Architecture Search for Anomaly Detection in Time-Series Data of Smart Buildings: A Reinforcement Learning Approach for Optimal Autoencoder Design
Bibliographic record
Abstract
The proliferation of Internet of Things (IoT) sensors in smart buildings has generated vast amounts of time series data, offering valuable insights when properly leveraged. We propose to use this data to identify abnormal behaviors and deviations in temporal data which will enable the detection of anomalies related to power consumption, control system failures, and sensor malfunctions. To achieve this, we propose a reconstruction-based anomaly detection framework utilizing autoencoders where we train the model on anomaly-free samples, minimizing the error between the original and reconstructed sequences. Then, by setting a threshold on the reconstruction error, abnormal sequences can be distinguished from the predominant regular patterns observed in the majority of the time windows. Moreover, to address the challenge of selecting a suitable autoencoder architecture, a Reinforcement Learning-based Neural Architecture Search (RLNAS) approach is employed to explore a manually defined search space and discover the best neural configuration by learning through trial and error. Experimental results on two custom anomaly detection datasets demonstrate competitive performance, showcasing the effectiveness of this approach in discovering effective architectures that may not be immediately apparent or intuitive.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".