Research map / Time series / Causal and generative time series
Conformal Time-series Forecasting
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add Bonferroni correction (alpha/H) to calibration (ablation) | CF-RNN · MIMIC-III | joint coverage (%): 89.0 -> 94.0 | Helped |
| add Bonferroni correction (alpha/H) to calibration (ablation) | CF-RNN · EEG | joint coverage (%): 59.4 -> 96.5 | Helped |
| add Bonferroni correction (alpha/H) to calibration (ablation) | CF-RNN · COVID-19 | joint coverage (%): 55.5 -> 89.7 | Helped |
| use conformal calibration instead of MC dropout | RNN forecaster · COVID-19 | joint coverage (%): 0.0 -> 89.7 | Helped |
| use conformal calibration instead of multi-quantile RNN | RNN forecaster · EEG | joint coverage (%): 48.0 -> 96.5 | Helped |
| use conformal calibration instead of multi-quantile RNN | RNN forecaster · EEG | interval width: 21.39 -> 61.86 | Hurt |
| use CF-RNN instead of blockwise jackknife RNN | RNN forecaster · Synthetic static noise n=1 | interval width: 98.45 -> 16.45 | Helped |
| use CF-RNN instead of blockwise jackknife RNN | RNN forecaster · Synthetic time-dependent noise n=4 | interval width: 33.24 -> 40.74 | Hurt |
| use CF-RNN instead of MQ-RNN | RNN forecaster · MIMIC-III | joint coverage (%): 89.3 -> 94.0 | Helped |
Closest papers by meaning
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- Relational Conformal Prediction for Correlated Time Series (ICML 2025 · similarity 0.86)
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