Forecasting stock indexes based on a revised grey model and the ARMA model
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
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.
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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