Enhancing forecasting accuracy in dynamic environments via PELT-driven drift detection and model adaptation
Why this work is in the frame
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Bibliographic record
Abstract
Time series forecasting models often experience a decline in prediction accuracy due to data drift, which occurs when the underlying data distribution changes over time. To address this challenge, this study proposes an adaptive forecasting framework that integrates drift detection with targeted model retraining to compensate for drift effects. The framework utilizes the Pruned Exact Linear Time (PELT) algorithm to identify drift points within the feature space of time series data. Once drift intervals are detected, selective retraining is applied to prediction models using Multilayer Perceptron and Lasso Regressor architectures, allowing the models to adjust to changing data patterns. To assess effectiveness, the method is applied to a synthetic dataset for ideal conditions and a real-world heating, ventilation, and air-conditioning dataset that reflects practical challenges and complex dependencies. Initial baseline models were developed without drift detection using extensive feature engineering. After integrating drift-aware retraining, the multilayer perceptron (MLP) model achieved a 27% reduction in Mean Absolute Error and a 4–5% increase in R ² on the real-world dataset, while even greater improvements were observed on the synthetic dataset. Similar enhancements were achieved with the Lasso Regressor. These results highlight the robustness and generalizability of incorporating drift detection and adaptive retraining to sustain forecasting accuracy across diverse domains.
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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 | 0.001 |
| 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 it