Jämför metoder
Granska de valda metoderna sida vid sida; rader som skiljer sig är markerade.
| Mamba (tillståndsrumsmodell)× | TimeGPT× | |
|---|---|---|
| Ämnesområde | Djupinlärning | Djupinlärning |
| Familj | Machine learning | Machine learning |
| Ursprungsår | 2023 | 2023 |
| Upphovsperson≠ | Albert Gu | Fabio Garza |
| Typ | Neural network architecture | Neural network architecture |
| Ursprungskälla≠ | Gu, A., & Dao, C. (2023). Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.08956. link ↗ | Garza, F., & White, C. W. (2023). TimeGPT-1: A Time Series Foundation Model. In ICML 2024 Time Series Workshop. link ↗ |
| Alias≠ | Mamba, State space models, Selective state space | TimeGPT-1, Time series GPT |
| Närliggande | 4 | 4 |
| Sammanfattning≠ | 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. | 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. |
| ScholarGateDatamängd ↗ |
|
|