Winner-takes-all for Multivariate Probabilistic Time Series Forecasting

2025/06/05 by Adrien Cortés, Cortés, Adrien, Rehm, Rémi +2 · 1 voice · 2 citations
Computer Science · Decision Sciences · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Forecasting Techniques and Applications #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning in Healthcare #Stock Market Forecasting Methods

paper · pdf · doi:10.48550/arxiv.2506.05515

openalex publication_date 2025/06/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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 predictions. 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, presenting it as an efficient method to predict diverse futures, which we relate to its implicit quantization objective. We provide insights into our approach using synthetic data and evaluate it on real-world time series, demonstrating its promising performance at a light computational cost.

Citations

• Annealed Multiple Choice Learning: Overcoming limitations of Winner-takes-all with annealing
• Winner-takes-all learners are geometry-aware conditional density estimators
• TACTiS-2: Better, Faster, Simpler Attentional Copulas for Multivariate Time Series
• TACTiS: Transformer-Attentional Copulas for Time Series
• Trajectory-wise Multiple Choice Learning for Dynamics Generalization in Reinforcement Learning
• DiffWave: A Versatile Diffusion Model for Audio Synthesis
• Denoising Diffusion Probabilistic Models
• Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows
• DMCL: Distillation Multiple Choice Learning for Multimodal Action Recognition
• Normalizing Flows for Probabilistic Modeling and Inference
• High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula Processes
• Neural Autoregressive Flows
• Confident Multiple Choice Learning
• DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks
• Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks
• Learning in an Uncertain World: Representing Ambiguity Through Multiple Hypotheses
• WaveNet: A Generative Model for Raw Audio
• Gaussian Error Linear Units (GELUs)
• Stochastic Multiple Choice Learning for Training Diverse Deep Ensembles
• Density estimation using Real NVP
• Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
• Sequence to Sequence Learning with Neural Networks
• Generating Sequences With Recurrent Neural Networks
• Centroidal Voronoi Tessellations: Applications and Algorithms
• Long Short-Term Memory
• Least squares quantization in PCM

Cited by

• TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting
• From Samples to Scenarios: A New Paradigm for Probabilistic Forecasting

Discussions

• Interested in time series forecasting or data uncertainty quantification? Check out our latest paper with Adrien Cortés at @icmlconf ! Paper: arxiv.org/abs/2506.05515 Code: github.com/Victorletzel... [bsky, 7 points, 1 comments]

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• TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting
• Are KANs Effective for Multivariate Time Series Forecasting?