Bibliographic record
Abstract
Predicting stock price has special importance for shareholders to gain the maximum profit and they have always sought for logical and accurate strategies to predict it.Data mining techniques, in addition to data collection and management, involve analyzing and predicting.Recognizing the current patterns and unknown relationships among the data help us in the predicting.Several models have been developed for predicting by using time series by researchers in the recent years.Given the studies conducted in this regard, it can be realized that one of the important issues in these models is the way of determining the fuzzy intervals to explain the model and to predict.Three models were introduced in this research using combination of fuzzy time series and cuckoo optimization algorithms (FTS-COA), and combination of fuzzy time series and particle swarm algorithm (FTS-PSO), and combination of fuzzy time series and firefly algorithm (FTS-FOA).Finally, to compare the introduced models, findings of these three models are compared.Findings reflect the superiority of the (FTS-COA) model compared to the two models introduced.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.001 |
| Insufficient payload (model declined to judge) | 0.957 | 0.970 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".