Recursive deep learning with multi-scale attention for energy demand dynamics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
• Fully learnable filters adapt to nonstationary and multi-scale signal dynamics. • Joint training of denoising and modeling enhances end-to-end accuracy and stability. • Entropy-aware gating fuses multi-stage forecasts with uncertainty-based weighting. • Delivers robust energy price forecasting under volatility and regime shifts. • Achieves up to 85% RMSE and 99% MAPE reduction over 50 existing models. Nonstationary and multi-scale time series challenge traditional decomposition-based and denoising techniques. These approaches often rely on fixed transformations or shallow residual modeling, limiting adaptability to evolving dynamics likes energy price forecasting. This work introduces an end-to-end recursive deep learning architecture that preserves the classical estimation loop while rendering all state-space parameters data-adaptive at every stage and time step. The parameters are synthesized by a lightweight neural generator conditioned on recent residual behavior and cross-scale context, enabling reconfiguration as regimes shift. Robustness is ensured through two design choices: a stability-preserving parameterization of state evolution that prevents drift and explosions, and innovation-driven, heteroskedastic noise modeling that automatically adjusts to volatility. At each stage, a trend is extracted, modeled by a shared deep multilayer perceptron (DMLP), and fused through an entropy-aware module that weights stage contributions, yielding a closed loop that progressively removes structure from the residuals. On the WTI crude oil dataset, compared to a baseline DMLP (MAE = 1.879, MAPE = 1.825 %, RMSE = 2.457), the proposed model achieved a 57.1 % reduction in MAE, 56.7 % in MAPE, and 58.0 % in RMSE (MAE = 0.806, MAPE = 0.787 %, RMSE = 1.031). It also outperformed over 50 state-of-the-art models, achieving up to 85 % lower RMSE and over 99 % reduction in MAPE. The framework is compact, reproducible, and suitable for energy-economics analytics under regime shifts and noise.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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