The optimized GPM(1,1) for forecasting small sample oscillating series
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
Purpose The purpose of this paper is to provide a modeling approach using grey power model with first‐order one‐variable (abbreviated as GPM(1,1)) for forecasting small sample oscillating series. Design/methodology/approach An optimization method is used to determine the initial value in GPM(1,1) model, and furthermore, the power value in the model is optimized by utilizing a non‐linear programming model. An operations research software LINGO is employed to solve the non‐linear optimization model. Findings The results show that the optimized GPM(1,1) model can flexibly adjust the parameters to make the forecasting results more in line with the actual data; therefore, for a given small sample oscillating series, if an appropriate way to find the optimal parameters is taken, accurate predictions should be obtained. Practical implications The modeling approach proposed in the paper can be used to forecast new product sales, new industry development trend, equipment remaining life, disaster emergency material demand, etc. Originality/value The paper extends the application range of the grey model for forecasting small sample oscillating series by using grey power model GPM(1,1).
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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 | 0.023 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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