CLaST通过对比学习提升时间序列预测的上下文感知能力。
CLaST: Context-aware Contrastive VAE for Probabilistic Time Series Forecasting
- 引入对比损失函数，增强嵌入表示的上下文相似性
- 短时预测CRPS提升16.4%，长时预测CRPS提升48.6%
- 适合需要高精度概率预测的能源、金融等领域
概率预测模型广泛应用于能源系统、金融、医疗和交通等领域的时序预测。近年来，深度生成模型在概率预测中表现优异，但传统方法难以捕捉内部时序依赖，导致潜在表示表达能力有限。为此，我们提出CLaST，一种用于多变量时序概率预测的变分自编码器框架。不同于现有生成模型，CLaST通过对比损失函数学习保留观测间上下文相似性的嵌入表示。在九个主流基准上的实验表明，CLaST始终优于强基线方法。在短时预测任务中，相比第二佳方法，CRPS提升最高达16.4%，NMAE提升14.4%；在长时预测中，整体表现更优，CRPS与NMAE分别超过第二佳方法48.6%和25.1%。
原文摘要 · Abstract (English)
Probabilistic forecasting models are widely used for time series forecasting in domains such as energy systems, finance, medicine, and transportation. In recent years, deep generative models have shown strong results on probabilistic forecasting, yet many conventional approaches struggle to capture internal temporal dependencies, leading to latent representations with limited expressive power. To address this limitation, we propose \textit{CLaST}, a VAE framework for probabilistic multivariate time series forecasting. Unlike existing generative models, CLaST learns embeddings that preserve contextual similarity between observations through our contrastive loss function. Experiments across nine widely adopted benchmarks demonstrate that CLaST consistently surpasses strong baseline methods. In short-term forecasting tasks, our approach achieves improvements of up to $16.4\%$ in CRPS and $14.4\%$ in NMAE over the second-best method. Furthermore, in long-term prediction CLaST attains superior overall performance, exceeding the second-best method by up to $48.6\%$ and $25.1\%$ in CRPS and NMAE, respectively.
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