An Efficient Dynamic Auto-Regressive CCA for Time Series Imputation With Irregular Sampling
Why this work is in the frame
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Bibliographic record
Abstract
System dynamics are inevitable in industrial processes due to factors such as ambient disturbances and controller tuning. Accurate modeling of these dynamics are of key importance for subsequent process analysis and anomaly detection, and dynamic latent variable methods are widely adopted since they retain good interpretability. However, only dynamic cross-correlations are modeled in existing methods, leaving a large portion of quality information unexploited. In this work, an efficient dynamic auto-regressive canonical correlation analysis (EDACCA) method is proposed with a modified auto-regressive exogenous model to extract dynamics in both auto-correlations and cross-correlations. The flexibility and efficiency of EDACCA are improved with the design of weighting parameters and the economic singular value decomposition. EDACCA is further adapted for multi-step ahead (MS) prediction and missing data imputation. Two industrial processes are employed to evaluate the prediction performance and imputation performance of EDACCA.Note to Practitioners—Different sampling rates are usually set for process and quality variables in industrial processes, which leads to less quality samples. Meanwhile, system dynamics are not fully exploited for dynamic predictive modeling in most existing algorithms. The focus of this study is to develop a customized data imputation method for different data volume of process and quality data. An efficient dynamic auto-regressive canonical correlation analysis (EDACCA) is designed to extract temporal relations between process and quality variables, which is also adapted for multi-step-ahead prediction purpose. An EDACCA based data imputation method is also proposed to impute incomplete data caused by irregular sampling rates.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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