Author

Roberto Corizzo

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Open access Oct 2026

GCN-FinBERT-LSTM: a graph convolutional deep fusion model for multimodal stock market prediction

Time series forecasting models play a crucial role as decision support tools in various real-world domains. Among them, stock market represents a notably complex domain, characterized by a rapidly evolving temporal nature and a multitude of factors influencing stock prices. While many machine learning-based approaches...

Pi Chen, Roberto Corizzo · International Journal of Dat... · 0 citations

Open access Sep 2026

SPIRAL: A Novel Time Series to Image (TS2I) Transformation Method for Vision-Based Anomaly Detection

SPIRAL is introduced, a novel TS2I transformation designed for TSAD that maps time series windows onto a two-arm Archimedean spiral, producing asymmetric representations that preserve temporal locality while eliminating redundancy with linear time complexity with respect to window length.

Mateusz Smendowski, Kamil Faber, Piotr Nawrocki et al. · Machine-mediated learning · 0 citations

Open access Sep 2026

Bridging Continual Learning and Green Cloud Computing: Foundations for Sustainable Time Series Anomaly Detection

This study forms temporal concept-incremental anomaly detection for both univariate and multivariate time series, introduces time-based and entity-based concept extraction strategies together with Wasserstein-distance diagnostics for inter- and intra-concept distribution drift, and defines an evaluation protocol that m...

Mateusz Smendowski, Roberto Corizzo, Nathalie Japkowicz et al. · Journal of Grid Computing · 0 citations

Preprint Aug 2026

PRISM: Powerful Time Series to Image (TS2I) Representations for Multivariate Anomaly Detection

PRISM is introduced, a plug-and-play meta-workflow enabling systematic construction and evaluation of image-based representations for multivariate TSAD and channelization is identified - how the channel dimension of multi-channel images is constructed - as a critical and previously understudied design dimension.

Mateusz Smendowski, Kamil Faber, Piotr Nawrocki et al. · 0 citations

#machine learning Preprint Sep 2026

MURAL: Multimodal Uncertainty-aware Recommendation via Adaptive edge Learning

Multimodal Graph Neural Networks have become standard for recommendation by augmenting sparse interaction data with content features. Yet current architectures face two bottlenecks: structural rigidity, from a reliance on static precomputed similarity graphs that cannot adapt to evolving preferences; and semantic fragi...

Ahmad Mousavi, Majid Alikhani, Yeon-Chang Lee et al. · 0 citations