Research map / Time series / Long-term forecasting
Probabilistic Transformer For Time Series Analysis
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add second stochastic layer (ablation) | one-layer ProTran · TRAFFIC | CRPSsum (lower better): 0.031 -> 0.028 | Helped |
| add context attention (ablation) | one-layer ProTran · TRAFFIC | CRPSsum (lower better): 0.033 -> 0.031 | Helped |
| add stochastic latent variables to w_t (vs deterministic) (ablation) | one-layer ProTran · TRAFFIC | CRPSsum (lower better): 0.041 -> 0.031 | Helped |
| use ProTran instead of TimeGrad | forecasting · TRAFFIC | CRPSsum (lower better): 0.044 -> 0.028 | Helped |
| use ProTran instead of TimeGrad | forecasting · ELECTRICITY, WIKIPEDIA | CRPSsum (lower better): 0.021 -> 0.016 (ELECTRICITY); 0.049 -> 0.047 (WIKIPEDIA) | No clear effect |
| use ProTran instead of DLow | human motion prediction · Human3.6M, HumanEva-I | ADE/FDE (lower better): ADE 0.425 -> 0.381, FDE 0.518 -> 0.491 (Human3.6M); ADE 0.251 -> 0.258, FDE 0.268 -> 0.255 (HumanEva-I) | Mixed |
Closest papers by meaning
- SAMformer: Unlocking the Potential of Transformers in Time Series Forecasting with Sharpness-Aware Minimization and Channel-Wise Attention (ICML 2024 · similarity 0.90)
- Latent Diffusion Transformer for Probabilistic Time Series Forecasting (AAAI 2024 · similarity 0.90)
- Considering Nonstationary within Multivariate Time Series with Variational Hierarchical Transformer for Forecasting (AAAI 2024 · similarity 0.90)
- HDT: Hierarchical Discrete Transformer for Multivariate Time Series Forecasting (AAAI 2025 · similarity 0.89)
- Timer: Generative Pre-trained Transformers Are Large Time Series Models (ICML 2024 · similarity 0.88)
- TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting (ICLR 2024 · similarity 0.88)
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