NFIG-X: Nonlinear Fuzzy Information Granule Series for Long-Term Traffic Flow Time-Series Forecasting
Bibliographic record
Abstract
Long-term time-series forecasting is an extensive research topic and is of great significance in many fields. However, the task of long-term time-series forecasting is accompanied by the problem of increasing cumulative error and decreasing time correlation. To overcome these shortcomings, this article proposes a prediction framework based on the nonlinear fuzzy information granule (NFIG) series, which can boost the long-term performance of most predictors. First, we propose the representation of the NFIG for the first time, replacing the linear core lines with nonlinear time-dependent curves. Second, we propose a temporal window splitting algorithm based on curvature equations and weighted directed graphs, which can not only merge temporal windows with the same trend but also cointegrate incremental data. Finally, the nonlinear trend fuzzy granulation can be employed as a data preprocessing module for various time-series predictors to achieve a better long-term forecasting performance. As a typical time-series forecasting task, the precise long-term forecast of traffic flow data can relieve the overburdened traffic system and improve the traffic environment to a certain extent. Thus, the proposed method is employed for the long-term traffic flow forecasting. Compared with existing forecasting models, which achieves superior performances.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".