The TriLS Approach for Drift-Aware Time-Series Prediction in IIoT Environment
Bibliographic record
Abstract
This article presents a novel drift-aware approach to multivariate time-series modeling in the nonstationary industrial Internet of Things environments. The three-layered three-state (TriLS) system enables cooperation between the gateway and the cloud toward the timely adjustment of a lightweight predictive model. Concept drift is detected by the cloud with the use of the extended adaptive windowing algorithm that operates on statistics of time sequences tracked by the gateway. This system is geared toward providing accurate predictions of nonstationary industrial processes for intelligent factory automation and safety. The proposed TriLS system is evaluated on records of recurring chemical processes collected at two plants and implemented on a Raspberry Pi board. TriLS achieves a lower prediction error than the reference adaptive schemes while reducing the computational effort and memory requirements for adaptation at the gateway by over 66% and 48%, respectively. It also reduces the volume of shared data between the gateway and the cloud by 40% –72% that is a significant cut on communications overhead.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".