FSNet: A Hybrid Model for Seasonal Forecasting
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
Load forecasting with low prediction error is essential to keep minimizing costs in generating and supplying power. It has many applications in energy production, distribution, and infrastructure construction. Because of the high autocorrelation and strong seasonality in load data, it is difficult to build robust and generalizable forecasting models. To address the problem, we propose a hybrid model, the Fourier Split NET (FSNET). The proposed model consists of two phases. A deseasonalization phase where the model uses the Fourier transform to isolate the seasonal component from the data using the fast Fourier transform. The second phase consists of training a simple linear model to replicate the seasonal behavior of the data and training a group of LSTM neural networks on different clusters of the data. The model uses statistical features to build separate LSTM models for different groups of data. We experimented on open datasets and obtained higher accuracy results compared to other forecasting approaches using different accuracy metrics.
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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