Research map / Time series / Long-term forecasting
Hierarchical Deep Generative Models for Multi-Rate Multivariate Time Series
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add multi-rate latent structure (MR-DMM) to DMM (ablation) | DMM · MIMIC-III forecasting | MSE: 1.530 -> 1.061 | Helped |
| add hierarchical latent layers with auxiliary connections to DMM (HDMM) (ablation) | DMM · MIMIC-III forecasting | MSE: 1.530 -> 1.047 | Helped |
| add learnable switch mechanism for multi-rate hierarchy (MR-HDMM vs HDMM) (ablation) | HDMM · MIMIC-III forecasting | MSE: 1.047 -> 0.996 | Helped |
| add learnable switches to HDMM (ablation) | HDMM · USHCN forecasting | MSE: 0.626 -> 0.591 | Helped |
| add switches to HDMM on low-rate forecasting (ablation) | HDMM · MIMIC-III LSR forecasting | MSE: 1.076 -> 0.911 | Helped |
| replace single-rate VAR with MR-HDMM | VAR · USHCN forecasting | MSE: 2.415 -> 0.591 | Helped |
| replace VAR with MR-HDMM on MIMIC-III LSR | VAR · MIMIC-III LSR forecasting | MSE: 0.802 -> 0.911 | Mixed |
| use MR-HDMM instead of HDMM for interpolation (ablation) | HDMM · MIMIC-III in-sample interpolation | MSE: 3.790 -> 3.582 | Helped |
| use MR-HDMM instead of MR-DMM for interpolation (ablation) | MR-DMM · USHCN interpolation | MSE: 0.696 -> 0.626 | Helped |
| use MR-HDMM instead of MICE imputation | MICE · MIMIC-III out-sample interpolation | MSE: 7.580×102 -> 2.921 | Helped |
| use MR-HDMM instead of Simple-Mean | Simple-Mean · MIMIC-III in-sample interpolation | MSE: 3.812 -> 3.582 | Helped |
| add multi-rate and hierarchy to DMM for generative fit (ablation) | DMM · USHCN | log-likelihood lower bound: 2.37 -> 33.62 | Helped |
Closest papers by meaning
- HDT: Hierarchical Discrete Transformer for Multivariate Time Series Forecasting (AAAI 2025 · similarity 0.90)
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- TimePFN: Effective Multivariate Time Series Forecasting with Synthetic Data (AAAI 2025 · similarity 0.86)
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- TimeCMA: Towards LLM-Empowered Multivariate Time Series Forecasting via Cross-Modality Alignment (AAAI 2025 · similarity 0.86)
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