Forecasting stock indexes based on a revised grey model and the ARMA model
Bibliographic record
Abstract
A hybrid grey model—autoregressive moving average ( GM-ARMA) model,constructed by combing the GM ( 1,1) model and the ARMA model,has two drawbacks.One drawback is that the GM-ARMA model may not be optimal since the traditional GM ( 1,1) model is not optimal.The other is that the GM-ARMA model does not combine two sub-models properly;this may also cause the GM-ARMA model to be suboptimal.This paper tries to first modify the GM ( 1,1) model by introducing 2 parameters,the grey dimension degree and white background value.A revised GM-ARMA model was constructed by optimizing all parameters in the GM ( 1,1) model and the ARMA model simultaneously.For convenience,we called this revised GM-ARMA model the RGM-ARMA model.Experimental results showed that the RGM-ARMA model has fewer prediction errors than the ARMA model or the GM-ARMA model and gives a new solution for construction of hybrid models.
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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 | 0.003 |
| 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".