PREVISOR SVR-LSSVR WAVELET NA PROJEÇÃO DE SÉRIES TEMPORAIS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Na literatura, e bem conhecido que o s metodos preditivos (ou previsores) support vector regression (SVR) e least square support vector regression (LSSVR) consistem em alternativas eficientes na projecao de series temporais (estocasticas) e que a decomposicao wavelet oferece vantagens atrativas no processo preditivo. Assim o sendo, utilizando programacao nao linear e combinacao linear de previsoes, propoe-se neste artigo um previsor hibrido que integra as seguintes abordagens: SVR, LSSVR e decomposicao wavelet. A fim de ilustra-lo, e utilizada a serie temporal Canadian lynx. Os resultados alcancados pelo previsor hibrido proposto alcancou maior nivel de acuracia que dezoito metodos competitivos.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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