Spatiotemporal Predictive Models for Irregularly Sampled Time Series
Bibliographic record
Abstract
To perform the long-term spatiotemporal sequence prediction (SSP) task with irregular time sampling assumptions, we build the sequence-to-sequence models based on the Trajectory Gated Recurrent Unit (TrajGRU) network and our proposed deep learning modules. First, we design a novel attention mechanism, namely Motion-based Attention (MA), and insert it into the TrajGRU network to create the TrajGRU-Attention model. In particular, the TrajGRU-Attention model can alleviate the impact of the vanishing gradient, which leads to the blurry effect in the long-term predictions and handle irregularly sampled time series. Second, leveraging the advances in Neural Ordinary Differential Equation (NODE) technique, we propose the TrajGRU-Attention-ODE model, which can be applied in continuous-time applications. To evaluate the performance of the proposed models, we select four available spatiotemporal datasets with increasing complexity levels, including the MovingMNIST, MovingMNIST++, KTH Action, and TAASRAD19. Our models outperform the state-of-the-art NODE model and generate better results than the standard TrajGRU model for SSP tasks with different types of time sampling.
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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 | 0.000 | 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 | 0.000 | 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 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".