Time series forecasting with variable-centric spectral transformer
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
Abstract Although Transformers have demonstrated remarkable success in natural language processing, their direct application to time series forecasting faces significant challenges. Traditional Transformers treat tokens as semantic units (e.g. words), whereas tokens in time series (e.g. individual data points) lack semantic coherence, leading to suboptimal modeling of long-term dependencies. Additionally, conventional positional encodings struggle to capture periodic patterns in temporal data, and the quadratic computational complexity of self-attention mechanisms becomes prohibitive for large-scale sequences. We propose ICLformer, the first transformer variant that integrates transposed tokens and complex frequency domain linear interpolation, to address two critical limitations of conventional Transformers in time series forecasting: (1) redefining temporal representations via transposed tokens to model global dependencies across time steps in a variable-centric manner; (2) introducing frequency domain interpolation to preserve periodic features while achieving data imputation and noise reduction with linear computational complexity. Our model excels in accuracy, stability, and computational efficiency compared with traditional methods.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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