TimeMCL uses Winner-Takes-All training of multiple heads to quantize the conditional distribution of future time series, producing diverse forecasts at low inference cost.
ETS, Trf.TempFlow and Tactis2, columns are in gray because they don’t share the same backbone as the other baselines
1 Pith paper cite this work. Polarity classification is still indexing.
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Winner-takes-all for Multivariate Probabilistic Time Series Forecasting
TimeMCL uses Winner-Takes-All training of multiple heads to quantize the conditional distribution of future time series, producing diverse forecasts at low inference cost.