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Dish-TS: A General Paradigm for Alleviating Distribution Shift in Time Series Forecasting

AAAI 2023 · Time series › Causal and generative time series · approach: Probabilistic and conformal forecasting · 119 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add Dish-TSN-BEATS · Electricity multivariate, horizon 24MSE: 0.041 -> 0.032Helped
Add Dish-TSN-BEATS · ETTh1 multivariate, horizon 24MSE: 0.797 -> 0.790Helped
Add Dish-TSN-BEATS · ETTm2 multivariate, horizon 24MSE: 0.643 -> 0.634Helped
Add Dish-TSN-BEATS · Weather multivariate, horizon 24MSE: 2.557 -> 2.267Helped
Use Dish-TS instead of RevINAutoformer · ETTh1 multivariate, horizon 24MSE: 1.245 -> 1.018Helped
Increase lookback from 48 to 144 (ablation)N-BEATS + Dish-TS · Electricity, horizon 48MSE: 1.2862 -> 0.7309Helped
Increase lookback from 48 to 144 (ablation)N-BEATS + Dish-TS · ETTh1, horizon 48MSE: 0.5708 -> 0.5202Helped
Remove prior guidance (set alpha to 0) (ablation)Dish-TS · Long series forecasting, lengths greater than 168prediction errorHurt
Initialize Conet with uniform or average values instead of normal values (ablation)Dish-TS · ETTh1 and WeatherMSEMixed

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