The linguistic forecasting of time series based on fuzzy cognitive maps
Bibliographic record
Abstract
Most researches of time series forecasting mainly focus on the aspect of pursuing the numerical forecasting precision by constructing the quantitative model. But in the real world, precision is sometimes not necessary for perceiving and reasoning of human, and the qualitative forecasting of time series is able to satisfy requirement of some decision problems. In this paper, a new qualitative forecasting method is proposed, which combines the fuzzy c-means clustering algorithm, fuzzy cognitive map (FCM) and the real-coded genetic algorithm (RCGA). The fuzzy c-means clustering algorithm is used to extract linguistic label, transform the original time series into the fuzzy time series and construct the framework of FCM, automatically. The RCGA algorithm is adopted to learn weights of constructed FCM for modeling the formed fuzzy time series. Finally, a fully learned fuzzy cognitive map is exploited to carry out linguistic forecast by iterations. The proposed forecasting method is applied to forecast the enrollments of university of Alberta on the linguistic level, whose results show the feasibility and effectiveness of proposed method.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| 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".