A Comparative Study on Deep Learning-Based: Temperature Prediction Models: Performance Evaluation of CNN, Transformer and Random Forest
Why this work is in the frame
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Bibliographic record
Abstract
Accurate short-term temperature prediction is of great significance in fields such as agricultural production and disaster prevention and mitigation. This study aims to explore the performance differences among three models—Convolutional Neural Network (CNN), Transformer, and Random Forest (RF)—in short-term temperature prediction tasks, providing a reference for model selection and optimization in meteorological forecasting. Based on the Beijing PM2.5 dataset, the research constructs supervised learning samples through data preprocessing (using the temperature sequence of the past 24 hours as input to predict the temperature at the 25th hour) and trains and evaluates the three models under unified experimental configurations. The results show that all three models can achieve high-precision predictions. Among them, Random Forest performs the best , with significant advantages in error control, noise resistance, and high training efficiency. CNN follows and excels at capturing local short-term fluctuation features. Transformer , although capable of modeling long-range dependencies, performs slightly inferior with the current dataset. The study reveals that traditional machine learning models still have practical value in resource-constrained scenarios, while deep learning models can further improve accuracy when sufficient data is available. Model fusion and the introduction of multiple factors may be future optimization directions.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it