Ensemble Empirical Mode Decomposition for time series prediction in wireless sensor networks
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Bibliographic record
Abstract
This paper outlines the use of Ensemble Empirical Mode Decomposition (EEMD) as a preprocessing step in wireless sensor network time series prediction using support vector machines. Inherent adaptive data analysis approach of the decomposition process makes the system robust to signals driven from non-linear and non-stationary processes. We propose two variants of the hybrid model called EEMD-SVM and EEMD-SVM-SUM and compare them with the stand-alone use of support vector machines for one-step ahead prediction. Root mean square error and correlation coefficients are used for performance comparison. Results indicate that the hybrid models enhance prediction accuracy as the original complex sensed phenomenon is decomposed into several simpler components which reduces the computational complexity of the support vector machines and increases their class separability.
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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 | 0.000 |
| 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 it