9 citations · 9 across the 6 of their papers we have counts for
6 papers
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…
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…
U-Former ODE: Fast Probabilistic Forecasting of Irregular Time Series
Ilya Kuleshov, Alexander Marusov, Alexey Zaytsev
Probabilistic forecasting of irregularly sampled time series is crucial in domains such as healthcare and finance, yet it remains a formidable challenge. Existing Neural Controlled…
A theoretical framework for self-supervised contrastive learning for continuous dependent data
Alexander Marusov, Aleksandr Yugay, Alexey Zaytsev
Self-supervised learning (SSL) has emerged as a powerful approach to learning representations, particularly in the field of computer vision. However, its application to dependent d…
Long-term drought prediction using deep neural networks based on geospatial weather data
Alexander Marusov, Vsevolod Grabar, Yury Maximov +3
The problem of high-quality drought forecasting up to a year in advance is critical for agriculture planning and insurance. Yet, it is still unsolved with reasonable accuracy due t…
Non-contrastive representation learning for intervals from well logs
Alexander Marusov, Alexey Zaytsev
The representation learning problem in the oil & gas industry aims to construct a model that provides a representation based on logging data for a well interval. Previous attempts…