ICML 2025

Winner-takes-all for Multivariate Probabilistic Time Series Forecasting
Adrien Cort´es * R´emi Rehm Victor Letzelter * 1 2
Abstract
We introduce TimeMCL, a method leveraging the
Multiple Choice Learning (MCL) paradigm to
forecast multiple plausible time series futures.
Our approach employs a neural network with
multiple heads and utilizes the Winner-Takes-All
(WTA) loss to promote diversity among predic-
tions. MCL has recently gained attention due to
its simplicity and ability to address ill-posed and
ambiguous tasks. We propose an adaptation of
this framework for time-series forecasting, pre-
senting it as an efficient method to predict diverse
futures, which we relate to its implicit quanti-
zation objective. We provide insights into our
approach using synthetic data and evaluate it on
real-world time series, demonstrating its promis-
ing performance at a light computational cost.
1. Introduction
Predicting the weather of the upcoming weekend or the
stock prices of next month with perfect accuracy would un-
doubtedly be useful. Unfortunately, time-series forecasting
is a highly ill-posed problem. In many cases, the available
input information is insufficient to reduce our uncertainty
about the estimation of the underlying stochastic process
and the data itself may contain noise. Consequently, the best
a forecaster can do is estimate plausible future trajectories,
ideally along with the probability of each outcome.
Because temporal data are highly structured and typically
come with input–output pairs that require no additional man-
ual annotation, autoregressive neural networks have become
the de facto standard for forecasting high-dimensional time
series from historical data and exogenous covariates (Ran-
gapuram et al., 2018; Salinas et al., 2019; Benidis et al.,
2020). To capture predictive uncertainty, practitioners often
*Equal contribution
1LTCI, T´el´ecom Paris, Institut Polytech-
nique de Paris, France 2Valeo.ai, Paris, France. Correspondence
to: Adrien Co...