Author

Guldana Taganova

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Open access Aug 2026

A Hybrid ARX and Deep Sequence Learning Architecture for Multivariate Industrial Time-Series Forecasting: An Application in Fluid Catalytic Cracking

Multi-parametric industrial time-series forecasting is important for predictive monitoring, soft sensing, and decision support in complex process industries. This study proposes a process-informed hybrid ARX-residual deep sequence architecture for short-horizon multi-output forecasting of fluid catalytic cracking unit...

G. Uskenbayeva, Guldana Taganova, A. Shukirova et al. · Algorithms · 0 citations

Open access Aug 2026

A Physics-Informed Bayesian Framework for Calibrated, Multi-Horizon Forecasting of Solar, Wind, and Hybrid Renewable Generation

Renewable-generation forecasting is a probabilistic time-series problem in which point accuracy alone is insufficient for operational decision-making. This study proposes a physics-informed Bayesian framework for multi-horizon forecasting of solar, wind, and total renewable generation. The task is formulated as a 15-di...

Assem Shayakhmetova, N. Tasbolatuly, Guldana Taganova et al. · Mathematics · 0 citations

Open access Jul 2026

B-TGPRF: A Bayesian Temporal Graph Probabilistic Model for Calibrated Overload-Risk Forecasting in Power Transmission Grids

Modern power transmission grids are increasingly operated under volatile load, variable generation, and near-limit line-flow conditions. In such environments, deterministic line-flow forecasting is insufficient for operational decision support because operators require calibrated risk probabilities, uncertainty interva...

Assem Shayakhmetova, N. Tasbolatuly, Guldana Taganova et al. · Algorithms · 0 citations