Forecasting the Taiwan Stock Market with a Novel Momentum-based Fuzzy Time-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
Fuzzy time-series models have been utilized in making reasonably accurate predictions in many areas, such as academic enrollments, weather forecasting and stock markets. To refine past fuzzy time-series models, this paper proposes a new model, which employs the concepts of ¡°momentum¡± along with Chebyshev¡¯s theorem in the forecasting process. The proposed model applies a ¡°momentum¡± index to generate forecasting rules (fuzzy logical relationships) to reduce the probability of rules not being found in cases where no rules are available to forecast a testing dataset. Chebyshev¡¯s theorem is adopted to define a ¡°reasonable¡± universe of discourse for the observations in a training dataset. From the refined process, two types of universe, symmetrical and asymmetrical, are given. To verify the proposed model, this paper employs experimental datasets, derived from a seven-year period of the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX). Model comparison results show that the proposed model surpasses in accuracy one traditional fuzzy time-series model and two advanced models, based on neural networks and rough set algorithms.
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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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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