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.
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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.