Bidirectional Temporal-Aware Modeling with Multi-Scale Mixture-of-Experts for Multivariate Time Series Forecasting
Bibliographic record
Abstract
Recent advances in deep learning have significantly boosted performance in multivariate time series forecasting (MTSF). While many existing approaches focus on capturing inter-variable (a.k.a. channel-wise) correlations to improve prediction accuracy, the temporal dimension, particularly its rich structural and contextual information, remains underexplored. In this paper, we propose BIM3, a novel framework that integrates BIdirectional temporal-aware modeling with Multi-Scale Mixture-of-Experts for MTSF. First, unlike existing methods that treat historical and future temporal information independently, we introduce a novel Timestamp Dual Cross-Attention Module, which employs a symmetric cross-attention mechanism to explicitly capture bidirectional temporal dependencies through timestamp interactions. Second, to address the complex and scale-varying temporal patterns commonly found in multivariate time series, we move beyond recent multi-scale forecasting models that share parameters across all channels and fail to capture channel-specific dynamics. Instead, we design a Multi-Scale Feature Extract Mixture-of-Experts module that adaptively routes time series to specialized experts based on their temporal characteristics. Extensive experiments on multiple real-world datasets show that BIM3 consistently outperforms state-of-the-art methods, highlighting its effectiveness in capturing both temporal structure and inter-variable diversity.
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How this classification was reachedexpand
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".