mlsanityData to 14 Oct 2026

Winner-takes-all for Multivariate Probabilistic Time Series Forecasting

arXiv 2506.05515 ↗First posted 5 Jun 2025cs.LG cs.AI stat.MLcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 25 of its 56 references that are in the map. First posted on arXiv 5 Jun 2025.

Builds on

25 of 56 references.

References · 25

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
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  3. 2014Sequence to Sequence Learning with Neural Networkscited by 2,986
  4. 2014Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modelingcited by 2,367
  5. 2016Density estimation using Real NVPcited by 2,042
  6. 2016WaveNet: A Generative Model for Raw Audiocited by 1,722
  7. 2020DiffWave: A Versatile Diffusion Model for Audio Synthesiscited by 1,354
  8. 2019Normalizing Flows for Probabilistic Modeling and Inferencecited by 1,323
  9. 2017DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networkscited by 1,013
  10. 2017Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networkscited by 879
  11. 2013Generating Sequences With Recurrent Neural Networkscited by 554
  12. 2021Monash Time Series Forecasting Archivecited by 267
13 more
  1. 2018Neural Autoregressive Flowscited by 208
  2. 2019High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula Processescited by 148
  3. 2020Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flowscited by 144
  4. 2016Learning in an Uncertain World: Representing Ambiguity Through Multiple Hypothesescited by 97
  5. 2016Stochastic Multiple Choice Learning for Training Diverse Deep Ensemblescited by 77
  6. 2022TACTiS: Transformer-Attentional Copulas for Time Seriescited by 38
  7. 2020Trajectory-wise Multiple Choice Learning for Dynamics Generalization in Reinforcement Learningcited by 35
  8. 2019DMCL: Distillation Multiple Choice Learning for Multimodal Action Recognitioncited by 30
  9. 2023TACTiS-2: Better, Faster, Simpler Attentional Copulas for Multivariate Time Seriescited by 26
  10. 2017Confident Multiple Choice Learningcited by 24
  11. 2023Resilient Multiple Choice Learning: A learned scoring scheme with application to audio scene analysiscited by 12
  12. 2024Annealed Multiple Choice Learning: Overcoming limitations of Winner-takes-all with annealingcited by 12
  13. 2024Winner-takes-all learners are geometry-aware conditional density estimatorscited by 6

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

  1. 2025From Samples to Scenarios: A New Paradigm for Probabilistic Forecastingcited by 10
  2. 2025TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecastingcited by 3
  3. 2026PrismFlow: Residual Dynamics for Flow Matching in Time-Series Generationcited by 1
  4. 2026Signature-Kernel Based Evaluation Metrics for Robust Probabilistic and Tail-Event Forecastingcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Channel-aware Contrastive Conditional Diffusion for Multivariate Probabilistic Time Series Forecasting
  2. 2026MemCast: Memory-Driven Time Series Forecasting with Experience-Conditioned Reasoning
  3. 2025Efficient Multivariate Time Series Forecasting via Calibrated Language Models with Privileged Knowledge Distillation
  4. 2024Are KANs Effective for Multivariate Time Series Forecasting?
  5. 2021Learning from Multiple Time Series: A Deep Disentangled Approach to Diversified Time Series Forecasting
  6. 2025Probabilistic Forecasting via Autoregressive Flow Matching
  7. 2025Time Series Forecasting via Reasoning: A Slow-Thinking Approach with Reinforcement Fine-Tuned LLMs
  8. 2023Predict, Refine, Synthesize: Self-Guiding Diffusion Models for Probabilistic Time Series Forecasting