Confronta i metodi

	Mamba (Modello a Spazio degli Stati)	Reti Convoluzionali Grafiche Spazio-Temporali	TimeGPT
Campo	Apprendimento profondo	Apprendimento profondo	Apprendimento profondo
Famiglia	Machine learning	Machine learning	Machine learning
Anno di origine	2023	2018	2023
Ideatore	Albert Gu	Sijie Yan	Fabio Garza
Tipo	Neural network architecture	Neural network architecture	Neural network architecture
Fonte seminale	Gu, A., & Dao, C. (2023). Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.08956.	Yan, S., Xiong, Y., & Lin, D. (2018). Spatial temporal graph convolutional networks for skeleton-based action recognition. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 32).	Garza, F., & White, C. W. (2023). TimeGPT-1: A Time Series Foundation Model. In ICML 2024 Time Series Workshop.
Alias	Mamba, State space models, Selective state space	ST-GCN, Spatial-Temporal Graph CNN	TimeGPT-1, Time series GPT
Correlati	4	4	4
Sintesi	Mamba is a sequence model architecture introduced by Gu and Dao in 2023 that achieves linear-time complexity while maintaining strong performance on language modeling tasks. By combining state space models with input-dependent selectivity, Mamba addresses the quadratic complexity of transformers while preserving modeling power.	Spatial-Temporal Graph Convolutional Networks (ST-GCN) is an architecture introduced by Yan et al. in 2018 for skeleton-based action recognition. By modeling human skeletons as graphs where joints are nodes and bones are edges, ST-GCN applies graph convolutions across space and time to recognize actions from skeleton sequences.	TimeGPT is a time series foundation model introduced by Garza and White in 2023 that unifies forecasting, anomaly detection, and classification in a single pre-trained model. Inspired by large language models, TimeGPT is pre-trained on diverse time series and transfers well to downstream tasks with minimal fine-tuning.