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Hierarchical Deep Generative Models for Multi-Rate Multivariate Time Series

ICML 2018 · Time series › Long-term forecasting · approach: Graph-based multivariate forecasting · 26 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add multi-rate latent structure (MR-DMM) to DMM (ablation)DMM · MIMIC-III forecastingMSE: 1.530 -> 1.061Helped
add hierarchical latent layers with auxiliary connections to DMM (HDMM) (ablation)DMM · MIMIC-III forecastingMSE: 1.530 -> 1.047Helped
add learnable switch mechanism for multi-rate hierarchy (MR-HDMM vs HDMM) (ablation)HDMM · MIMIC-III forecastingMSE: 1.047 -> 0.996Helped
add learnable switches to HDMM (ablation)HDMM · USHCN forecastingMSE: 0.626 -> 0.591Helped
add switches to HDMM on low-rate forecasting (ablation)HDMM · MIMIC-III LSR forecastingMSE: 1.076 -> 0.911Helped
replace single-rate VAR with MR-HDMMVAR · USHCN forecastingMSE: 2.415 -> 0.591Helped
replace VAR with MR-HDMM on MIMIC-III LSRVAR · MIMIC-III LSR forecastingMSE: 0.802 -> 0.911Mixed
use MR-HDMM instead of HDMM for interpolation (ablation)HDMM · MIMIC-III in-sample interpolationMSE: 3.790 -> 3.582Helped
use MR-HDMM instead of MR-DMM for interpolation (ablation)MR-DMM · USHCN interpolationMSE: 0.696 -> 0.626Helped
use MR-HDMM instead of MICE imputationMICE · MIMIC-III out-sample interpolationMSE: 7.580×102 -> 2.921Helped
use MR-HDMM instead of Simple-MeanSimple-Mean · MIMIC-III in-sample interpolationMSE: 3.812 -> 3.582Helped
add multi-rate and hierarchy to DMM for generative fit (ablation)DMM · USHCNlog-likelihood lower bound: 2.37 -> 33.62Helped

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