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pro vyhledávání: '"Alippi"'
In processing multiple time series, accounting for the individual features of each sequence can be challenging. To address this, modern deep learning methods for time series analysis combine a shared (global) model with local layers, specific to each
Externí odkaz:
http://arxiv.org/abs/2410.14630
Within a prediction task, Graph Neural Networks (GNNs) use relational information as an inductive bias to enhance the model's accuracy. As task-relevant relations might be unknown, graph structure learning approaches have been proposed to learn them
Externí odkaz:
http://arxiv.org/abs/2405.19933
Modern graph representation learning works mostly under the assumption of dealing with regularly sampled temporal graph snapshots, which is far from realistic, e.g., social networks and physical systems are characterized by continuous dynamics and sp
Externí odkaz:
http://arxiv.org/abs/2404.19508
Virtual sensing techniques allow for inferring signals at new unmonitored locations by exploiting spatio-temporal measurements coming from physical sensors at different locations. However, as the sensor coverage becomes sparse due to costs or other c
Externí odkaz:
http://arxiv.org/abs/2402.12598
Given a set of synchronous time series, each associated with a sensor-point in space and characterized by inter-series relationships, the problem of spatiotemporal forecasting consists of predicting future observations for each point. Spatiotemporal
Externí odkaz:
http://arxiv.org/abs/2402.10634
Graph-based deep learning methods have become popular tools to process collections of correlated time series. Differently from traditional multivariate forecasting methods, neural graph-based predictors take advantage of pairwise relationships by con
Externí odkaz:
http://arxiv.org/abs/2310.15978
Autor:
Verna, Adriano, Alippi, Paola, Offi, Francesco, Barucca, Gianni, Varvaro, Gaspare, Agostinelli, Elisabetta, Albrecht, Manfred, Rutkowski, Bogdan, Ruocco, Alessandro, Paoloni, Daniele, Valvidares, Manuel, Laureti, Sara
Publikováno v:
ACS Applied Materials & Interfaces 14 (2022) 12766-12776; Correction: ACS Applied Materials & Interfaces 14 (2022) 24069
Nowadays a wide number of applications based on magnetic materials relies on the properties arising at the interface between different layers in complex heterostructures engineered at the nanoscale. In ferromagnetic/heavy metal multilayers, such as t
Externí odkaz:
http://arxiv.org/abs/2309.15663
Autor:
Jin, Ming, Koh, Huan Yee, Wen, Qingsong, Zambon, Daniele, Alippi, Cesare, Webb, Geoffrey I., King, Irwin, Pan, Shirui
Publikováno v:
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2024
Time series are the primary data type used to record dynamic system measurements and generated in great volume by both physical sensors and online processes (virtual sensors). Time series analytics is therefore crucial to unlocking the wealth of info
Externí odkaz:
http://arxiv.org/abs/2307.03759
Relationships among time series can be exploited as inductive biases in learning effective forecasting models. In hierarchical time series, relationships among subsets of sequences induce hard constraints (hierarchical inductive biases) on the predic
Externí odkaz:
http://arxiv.org/abs/2305.19183