用多重选择机制生成多种可能的时间序列未来。
Winner-takes-all for Multivariate Probabilistic Time Series Forecasting
- 采用多头神经网络与胜者为王损失函数，鼓励预测多样性。
- 在真实数据上表现良好，计算开销小。
- 适合需要多方案预测的场景，如金融或气象建模。
我们提出TimeMCL，一种基于多重选择学习（MCL）范式的多变量概率时间序列预测方法。该方法采用具有多个输出头的神经网络，并使用胜者为王（WTA）损失函数促进预测结果的多样性。MCL因其简单性和对病态、模糊任务的处理能力而受到关注。我们将此框架适配于时间序列预测，将其视为一种高效生成多样化未来预测的方法，并揭示其隐含的量化目标。通过合成数据分析提供方法洞察，并在真实世界时间序列数据上进行评估，结果表明该方法在计算成本较低的情况下仍具备出色性能。
原文摘要 · Abstract (English)
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.
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