CLaST: Context-aware Contrastive VAE for Probabilistic Time Series Forecasting
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...
| Authors | Alexander Marusov, Dmitry Anikin, Petr Sokerin et al. |
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| Primary category | cs.LG |
| Published | 20 August 2026 |
| Last updated at source | 24 September 2026 |
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