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Temporal Dependencies in Feature Importance for Time Series Prediction

ICLR 2023 · Time series › Causal and generative time series · approach: Robust forecasting and evaluation · 6 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
use WinIT instead of the strongest listed baseline, AFOfeature importance on Spike · SpikeAUPRC: 0.944_{\pm0.01} -> 1.0_{\pm0.0}Helped
use WinIT instead of Dynamaskfeature importance on Delayed-Spike · Delayed-SpikeAUPRC: 0.502_{\pm0.002} -> 0.996_{\pm0.009}Helped
use WinIT instead of Dynamaskfeature importance on State · StateAUPRC: \textbf{0.278}_{\pm0.003} -> 0.26_{\pm0.01}Hurt
use WinIT instead of Dynamaskfeature importance on MIMIC-III mortality · MIMIC-III mortality, top 5%AUC Drop: 0.068_{\pm0.004} -> \textbf{0.098}_{\pm0.008}Helped
use WinIT instead of Dynamaskfeature importance on MIMIC-III mortality · MIMIC-III mortality, k=50Pred. Change: 0.076_{\pm0.004} -> \textbf{0.079}_{\pm0.004}Helped
use KL divergence instead of prediction difference (ablation)WinIT · MIMIC-III mortality, STD masking, top 5%AUC Drop: 0.078_{\pm0.008} -> 0.076_{\pm0.006}Hurt
use KL divergence instead of prediction difference (ablation)WinIT · MIMIC-III mortality, STD masking, k=50AUC Drop: 0.076_{\pm0.006} -> 0.072_{\pm0.007}Hurt
use Jensen-Shannon divergence instead of prediction difference (ablation)WinIT · MIMIC-III mortality, STD masking, top 5%AUC Drop: 0.078_{\pm0.008} -> 0.075_{\pm0.007}Hurt
use Jensen-Shannon divergence instead of prediction difference (ablation)WinIT · MIMIC-III mortality, STD masking, k=50AUC Drop: 0.076_{\pm0.006} -> 0.072_{\pm0.007}Hurt
replace mean aggregation with max aggregation (ablation)WinIT · MIMIC-III mortality, STD masking, top 5%AUC Drop: 0.078_{\pm0.008} -> 0.066_{\pm0.008}Hurt
replace mean aggregation with max aggregation (ablation)WinIT · MIMIC-III mortality, STD masking, k=50AUC Drop: 0.076_{\pm0.006} -> 0.063_{\pm0.004}Hurt
increase the WinIT window size from 1 to 3 (ablation)WinIT · Delayed-SpikeAUPRCHelped

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