3 papers
cs.LG2026
DecoVAE: a Lightweight Interpretable Trend-Seasonal VAE Framework for Efficient Probabilistic Time Series Forecasting
Alexander Marusov, Dmitry Anikin, Alexey Zaytsev
Probabilistic time series forecasting remains challenging, largely because modeling distinct trend and seasonal dynamics requires specialized approaches. Existing methods often fai…
cs.LG2026
CLaST: Context-aware Contrastive VAE for Probabilistic Time Series Forecasting
Alexander Marusov, Dmitry Anikin, Petr Sokerin +3
Probabilistic forecasting models are widely used for time series forecasting in domains such as energy systems, finance, medicine, and transportation. In recent years, deep generat…
cs.LG2025
Concealed Adversarial attacks on neural networks for sequential data
Petr Sokerin, Dmitry Anikin, Sofia Krehova +1
The emergence of deep learning led to the broad usage of neural networks in the time series domain for various applications, including finance and medicine. While powerful, these m…