Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| TimesFM× | Chronos× | PatchTST× | |
|---|---|---|---|
| Campo | Aprendizaje profundo | Aprendizaje profundo | Aprendizaje profundo |
| Familia | Machine learning | Machine learning | Machine learning |
| Año de origen≠ | 2024 | 2024 | 2023 |
| Autor original≠ | Abhimanyu Das et al. (Google) | Abdul Fatir Ansari et al. (Amazon) | Nie, Y. et al. |
| Tipo≠ | Pre-trained decoder-only transformer for zero-shot time-series forecasting | Pre-trained language-model-based time-series forecaster | Transformer for time series forecasting |
| Fuente seminal≠ | Das, A., Kong, W., Sen, R., & Zhou, Y. (2024). A decoder-only foundation model for time-series forecasting. ICML. link ↗ | Ansari, A. F., Stella, L., Turkmen, C., Zhang, X., Mercado, P., Shen, H., et al. (2024). Chronos: Learning the language of time series. Transactions on Machine Learning Research. 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 ↗ |
| Alias≠ | Time-series Foundation Model, Google TimesFM, TimesFM forecaster, Zaman Serisi Temel Modeli | Chronos Forecasting Model, Amazon Chronos, Tokenized Time-Series LLM, Kronos Zaman Serisi Modeli | PatchTST — Yama Tabanlı Zaman Serisi Transformer, patch-based time series transformer, channel-independent transformer |
| Relacionados≠ | 3 | 2 | 3 |
| Resumen≠ | 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. | Chronos is a family of pre-trained probabilistic forecasting models introduced by Ansari et al. at Amazon in 2024. It adapts the language-model paradigm to time series by quantizing continuous values into discrete tokens, enabling a standard transformer to be trained on a large heterogeneous corpus of time-series data. The result is a zero-shot forecasting model that generalizes across domains without requiring dataset-specific retraining. | 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 datos ↗ |
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