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…
Efficient Neural Controlled Differential Equations via Attentive Kernel Smoothing
Egor Serov, Ilya Kuleshov, Alexey Zaytsev
Neural Controlled Differential Equations (Neural CDEs) provide a powerful continuous-time framework for sequence modeling, yet the roughness of the driving control path often restr…
Parameter-Efficient Neural CDEs via Implicit Function Jacobians
Ilya Kuleshov, Alexey Zaytsev
Neural Controlled Differential Equations (Neural CDEs, NCDEs) are a unique branch of methods, specifically tailored for analysing temporal sequences. However, they come with drawba…
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…