Using wavelet transformation and a GM-ARMA model to forecast stock index
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
During the process of building a hybrid model by combining wavelet decomposition and other techniques,there is no standard in terms of selecting a wavelet base function and decomposition level.The commonly used ways are usually based on the researcher's experience or several experiments instead of a quantitative approach.In addition,many hybrid models based on wavelet decomposition do not consider the interaction between sub models.Instead of estimating the parameters in all sub models as the whole,they estimate the parameters separately,which lead to that the prediction result is not optimal.In order to solve this problem,this paper first introduced two new parameters,wavelet functions and decomposition levels,then quantitatively estimated all the parameters as a whole for the purpose of building an optimal hybrid model.For convenience,the model was called the WGM-ARMA model because it combines the wavelet decomposition,grey model,as well as autoregressive integrated moving average(ARMA) model.Experimental results show that the hybrid model significantly reduces prediction errors.As a result,it can be concluded that the model in terms of forecasting stock index is valid and useful,along with the method used to construct the optimal hybrid model.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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