Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| TimesFM× | PatchTST× | |
|---|---|---|
| Área | Aprendizado profundo | Aprendizado profundo |
| Família | Machine learning | Machine learning |
| Ano de origem≠ | 2024 | 2023 |
| Autor original≠ | Abhimanyu Das et al. (Google) | Nie, Y. et al. |
| Tipo≠ | Pre-trained decoder-only transformer for zero-shot time-series forecasting | Transformer for time series forecasting |
| Fonte seminal≠ | Das, A., Kong, W., Sen, R., & Zhou, Y. (2024). A decoder-only foundation model for time-series forecasting. ICML. link ↗ | Nie, Y., Nguyen, N. H., Sinthong, P. & Kalagnanam, J. (2023). A Time Series is Worth 64 Words: Long-term Forecasting with Transformers. ICLR. link ↗ |
| Outros nomes≠ | Time-series Foundation Model, Google TimesFM, TimesFM forecaster, Zaman Serisi Temel Modeli | PatchTST — Yama Tabanlı Zaman Serisi Transformer, patch-based time series transformer, channel-independent transformer |
| Relacionados | 3 | 3 |
| Resumo≠ | TimesFM is a pre-trained foundation model for univariate time-series forecasting introduced by Abhimanyu Das, Weihao Kong, Rajat Sen, and Yichen Zhou from Google in 2024. The model adopts a decoder-only transformer architecture, similar in spirit to large language models, and is trained on a large corpus of real-world and synthetic time-series data. Its central innovation is the ability to perform accurate zero-shot forecasting across diverse domains without task-specific fine-tuning. | PatchTST is a patch-based Transformer architecture for time series forecasting, introduced by Nie and colleagues in 2023, that cuts each series into overlapping patches treated as tokens and processes channels independently. It balances computational efficiency with strong accuracy on long-horizon forecasting. |
| ScholarGateConjunto de dados ↗ |
|
|