Comparar métodos

	TimeGPT	Modelos de Difusión Latente	Mamba (modelo de espacio de estados)
Campo	Aprendizaje profundo	Aprendizaje profundo	Aprendizaje profundo
Familia	Machine learning	Machine learning	Machine learning
Año de origen	2023	2022	2023
Autor original	Fabio Garza	Robin Rombach	Albert Gu
Tipo	Neural network architecture	Neural network architecture	Neural network architecture
Fuente seminal	Garza, F., & White, C. W. (2023). TimeGPT-1: A Time Series Foundation Model. In ICML 2024 Time Series Workshop.	Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022). High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 10684-10695).	Gu, A., & Dao, C. (2023). Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.08956.
Alias	TimeGPT-1, Time series GPT	LDM, Stable Diffusion, Latent Diffusion	Mamba, State space models, Selective state space
Relacionados	4	4	4
Resumen	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.	Latent Diffusion Models (LDMs) are a generative approach introduced by Rombach et al. in 2022 that performs the diffusion process in a compressed latent space rather than pixel space, enabling efficient high-resolution image synthesis. By compressing images into a low-dimensional latent representation using a variational autoencoder, diffusion becomes computationally tractable while maintaining visual quality.	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.