Winner-takes-all for Multivariate Probabilistic Time Series Forecasting

• URL: http://arxiv.org/abs/2506.05515v1
• Date: Thu, 05 Jun 2025 18:56:14 GMT
• Title: Winner-takes-all for Multivariate Probabilistic Time Series Forecasting
• Authors: Adrien Cortés, Rémi Rehm, Victor Letzelter,
• Abstract summary: We introduce TimeMCL, a method leveraging the Multiple Choice Learning (MCL) paradigm to forecast plausible time series futures.<n>Our approach employs a neural network with multiple heads and utilizes the Winner-Takes-All (WTA) loss to promote diversity among predictions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• Explainable Multi-modal Time Series Prediction with LLM-in-the-Loop [63.34626300024294]
  TimeXL is a multi-modal prediction framework that integrates a prototype-based time series encoder.<n>It produces more accurate predictions and interpretable explanations.<n> Empirical evaluations on four real-world datasets demonstrate that TimeXL achieves up to 8.9% improvement in AUC.
  arXiv  Detail & Related papers  (2025-03-02T20:40:53Z)
• MGCP: A Multi-Grained Correlation based Prediction Network for Multivariate Time Series [54.91026286579748]
  We propose a Multi-Grained Correlations-based Prediction Network. It simultaneously considers correlations at three levels to enhance prediction performance. It employs adversarial training with an attention mechanism-based predictor and conditional discriminator to optimize prediction results at coarse-grained level.
  arXiv  Detail & Related papers  (2024-05-30T03:32:44Z)
• Successive Model-Agnostic Meta-Learning for Few-Shot Fault Time Series Prognosis [3.5573601621032944]
  We introduce a novel 'pseudo meta-task' partitioning scheme that treats a continuous time period of a time series as a meta-task. Employing continuous time series as pseudo meta-tasks allows our method to extract more comprehensive features and relationships from the data. We introduce a differential algorithm to enhance the robustness of our method across different datasets.
  arXiv  Detail & Related papers  (2023-11-04T02:07:47Z)
• Optimizing accuracy and diversity: a multi-task approach to forecast combinations [0.0]
  We present a multi-task optimization paradigm that focuses on solving both problems simultaneously. It incorporates an additional learning and optimization task into the standard feature-based forecasting approach. The proposed approach elicits the essential role of diversity in feature-based forecasting.
  arXiv  Detail & Related papers  (2023-10-31T15:26:33Z)
• Sinkhorn-Flow: Predicting Probability Mass Flow in Dynamical Systems Using Optimal Transport [89.61692654941106]
  We propose a new approach to predicting such mass flow over time using optimal transport. We apply our approach to the task of predicting how communities will evolve over time in social network settings.
  arXiv  Detail & Related papers  (2023-03-14T07:25:44Z)
• Optimal Latent Space Forecasting for Large Collections of Short Time Series Using Temporal Matrix Factorization [0.0]
  It is a common practice to evaluate multiple methods and choose one of these methods or an ensemble for producing the best forecasts. We propose a framework for forecasting short high-dimensional time series data by combining low-rank temporal matrix factorization and optimal model selection on latent time series.
  arXiv  Detail & Related papers  (2021-12-15T11:39:21Z)
• Cluster-and-Conquer: A Framework For Time-Series Forecasting [94.63501563413725]
  We propose a three-stage framework for forecasting high-dimensional time-series data. Our framework is highly general, allowing for any time-series forecasting and clustering method to be used in each step. When instantiated with simple linear autoregressive models, we are able to achieve state-of-the-art results on several benchmark datasets.
  arXiv  Detail & Related papers  (2021-10-26T20:41:19Z)
• Ensembles of Randomized NNs for Pattern-based Time Series Forecasting [0.0]
  We propose an ensemble forecasting approach based on randomized neural networks. A pattern-based representation of time series makes the proposed approach suitable for forecasting time series with multiple seasonality. Case studies conducted on four real-world forecasting problems verified the effectiveness and superior performance of the proposed ensemble forecasting approach.
  arXiv  Detail & Related papers  (2021-07-08T20:13:50Z)
• Probabilistic Time Series Forecasting with Implicit Quantile Networks [0.7249731529275341]
  We combine an autoregressive recurrent neural network to model temporal dynamics with Implicit Quantile Networks to learn a large class of distributions over a time-series target. Our approach is favorable in terms of point-wise prediction accuracy as well as on estimating the underlying temporal distribution.
  arXiv  Detail & Related papers  (2021-07-08T10:37:24Z)
• SMART: Simultaneous Multi-Agent Recurrent Trajectory Prediction [72.37440317774556]
  We propose advances that address two key challenges in future trajectory prediction. multimodality in both training data and predictions and constant time inference regardless of number of agents.
  arXiv  Detail & Related papers  (2020-07-26T08:17:10Z)
• Ambiguity in Sequential Data: Predicting Uncertain Futures with Recurrent Models [110.82452096672182]
  We propose an extension of the Multiple Hypothesis Prediction (MHP) model to handle ambiguous predictions with sequential data. We also introduce a novel metric for ambiguous problems, which is better suited to account for uncertainties.
  arXiv  Detail & Related papers  (2020-03-10T09:15:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.