Deep Learning has been successfully applied to many application domains, yet its
advantages have been slow to emerge for time series forecasting. For example, in the well …

Large pre-trained models excel in zero/few-shot learning for language and vision tasks but
face challenges in multivariate time series (TS) forecasting due to diverse data …

Purpose This study aims to critically evaluate the competitiveness of Transformer-based
models in financial forecasting, specifically in the context of stock realized volatility …

Transformer-based methods have achieved excellent results in the field of time series
forecasting due to their powerful ability to model sequences and capture their long-term …

Time series foundation models excel in zero-shot forecasting, handling diverse tasks without
explicit training. However, the advancement of these models has been hindered by the lack …

Time series foundation models have demonstrated impressive performance as zero-shot
forecasters. However, achieving effectively unified training on time series remains an open …

Hierarchical time series analysis requires probabilistic forecasting techniques to account for
inherent uncertainties. A probabilistic forecast proposes a range of potential outcomes. In …

В работе проводится исследование архитектур мобильных приложений и их
применение при проектировании и разработке программной системы менторинга …

This research evaluates Transformer-based models in financial forecasting, a field where
precision and market understanding are critical. Prompted by Zeng et al.(2023), the study …

Time series foundation models, such as MOIRAI, have shown exceptional zero-shot
forecasting capabilities. However, they enable cross-frequency learning by employing …