A Two‐Stage NLP‐Driven Framework for Interval‐Valued Carbon Price Prediction Using Sentiment Analysis and Error Correction
Bibliographic record
Abstract
ABSTRACT Accurate predictions of carbon prices are essential for efficient administration and stable operation of carbon markets. Previous studies have mostly focused on point or interval predictions based on point‐valued data. These approaches insufficiently capture the full extent of market volatility. In contrast, interval‐valued data, containing maximum and minimum values, enable more meaningful interval‐valued predictions and thus provide a more comprehensive assessment of uncertainty. However, as previous research in this direction is limited, this study proposes a two‐stage framework for interval‐valued prediction using interval‐valued data. During the initial prediction stage, natural language processing (NLP) techniques are employed to analyze textual data from social media to assess market sentiment. This unstructured data (UD) is then combined with structured data (SD) and fed into a convolutional neural network‐bidirectional long short‐term memory‐Attention (CNN‐BiLSTM‐Attention) mechanism to generate an initial prediction. During the error correction (EC) stage, deviations between the actual and initial predicted values are calculated. These error sequences are then predicted and incorporated into the initial prediction to refine the final results. Trading simulations indicate that the proposed SD‐UD‐CNN‐BiLSTM‐Attention‐EC model can reduce trading risk and improve trading returns.
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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 | 0.001 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".