Problem
Research questions and friction points this paper is trying to address.
probabilistic forecasting
temporal dependencies
expressive power
CLaST: Context-aware Contrastive VAE for Probabilistic Time Series Forecasting
🤖 AI Summary
为解决时间序列预测中捕捉内部时序依赖性的问题，提出了一种基于对比损失函数的VAE框架CLaST，实验显示其在短期和长期预测上均优于现有方法。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Context-aware Contrastive VAE
Probabilistic Time Series Forecasting
Contrastive Loss Function
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