TREND FILTERING: EMPIRICAL MODE DECOMPOSITIONS VERSUS ℓ<sub>1</sub> AND HODRICK–PRESCOTT
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Bibliographic record
Abstract
Considering the problem of extracting a trend from a time series, we propose a novel approach based on empirical mode decomposition (EMD), called EMD trend filtering. The rationale is that EMD is a completely data-driven technique, which offers the possibility of estimating a trend of arbitrary shape as a sum of low-frequency intrinsic mode functions produced by the EMD. Based on an empirical analysis of EMD, an automatic procedure is proposed to select the requisite intrinsic mode functions. The performance of the EMD trend filtering is evaluated on simulated time series containing different forms of trends. Comparing furthermore to two existing techniques (ℓ 1 -trend filtering and Hodrick–Prescott filtering), we observe that the EMD trend filtering performs very similarly, while it does not require assumptions on the form of the trend and it is free from estimation parameters. We also illustrate the performance of the technique on the S&P 500 index, as an example of real-world time series.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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