DistDF replaces biased MSE training in time-series forecasting with a tractable joint Wasserstein discrepancy that upper-bounds the desired conditional distributional alignment.
The bias of MSE from the negative log-likelihood of the label sequence givenXis expressed as: Bias = Y|X − ˆY|X 2 Σ−1 |X − Y|X − ˆY|X 2 .(7) where∥v∥ 2 Σ−1 |X =v ⊤Σ−1 |X v
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
DistDF: Time-Series Forecasting Needs Joint-Distribution Wasserstein Alignment
DistDF replaces biased MSE training in time-series forecasting with a tractable joint Wasserstein discrepancy that upper-bounds the desired conditional distributional alignment.