In the Figure below (on synthetic data), you can see how the model learns to align predictions with the target quantization over training steps.

To ensure the predictions are meaningfully different (not just slight variations), we use a Winner-Takes-All training strategy that updates the best-performing prediction per example. This leads to quantization properties, where the predictions serve as representative prototypes of the future

In our paper we introduce TimeMCL, a method designed to predict multiple plausible futures for time series data.

TimeMCL builds on a technique called Multiple Choice Learning, which trains a model to generate a diverse set of predictions rather than a single outcome.

When we try to predict what might happen in the future based on past data, we often find that there isn’t just one “right” answer — there could be several possible future scenarios.