Multiseries Featural LSTM for Partial Periodic Time-Series Prediction: A Case Study for Steel Industry
Bibliographic record
Abstract
Partial periodic time series are found in many areas. However, many partial periodic time-series prediction methods are unable to capture the feature dependence within the fluctuation of data. In this article, a multiseries featural long short-term memory (LSTM) is proposed. A novel template-matching method is used to extract specific periodic characteristics adaptively and restack the 1-D time series into multiseries featural structure data. The extended series is fed into a multivariable LSTM network to exploit the feature-temporal patterns for predictions. To enhance the long-term prediction performance, a period correction method is used to reduce the iteration errors caused by multistep prediction. To demonstrate the effectiveness of the proposed method, two classical partial periodic data sets and two byproduct gas data sets are studied here. Our results demonstrate that the proposed prediction method has advantages on prediction accuracy, especially for the critical structural features, that satisfies the requirements of the practically viable prediction.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".