Long-Term Prediction Model for Fuzzy Granular Time Series Based on Trend Filter Decomposition and Ensemble Learning
Bibliographic record
Abstract
In the realm of control theory, the complex task of long-term time series prediction has been profoundly transformed by the confluence of advancements in computer technology and machine learning. However, the application of fuzzy information granularity remains a significant challenge, primarily due to the potential for substantial data distortion. To address this limitation, we propose an innovative long-term prediction model based on granularity time series, which integrates $l_{1}$ -trend filter decomposition and integrated learning. The core of our model lies in a novel modal decomposition method that utilizes $l_{1}$ -trend filters and a validity function to meticulously extract valuable insights from the original time series, thereby enhancing the precision of data analysis while preserving the integrity of the original data. Furthermore, we introduce a groundbreaking formula to measure the similarity of fuzzy information granularity, classifying time series components into three distinct categories: trend, period, and noise. By applying distinct prediction strategies to each category, we construct an integrated learning model that leverages the strengths of each component. At the heart of our model is a multilinear information granularity prediction approach, which is based on trend time windows and utilizes the newly developed similarity measure. This method not only maintains the integrity of the original time series but also offers a more accurate representation of the similarity between information grains. Empirical results from publicly available datasets validate the superior performance of our proposed prediction model, demonstrating its potential to significantly enhance long-term time series prediction accuracy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".