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| TiRex: xLSTM을 활용한 제로샷 시계열 예측× | Chronos: 시계열 예측을 위한 토큰화된 파운데이션 모델× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2025 | 2024 |
| 창시자≠ | NX-AI (xLSTM team) | Abdul Fatir Ansari et al. (Amazon) |
| 유형≠ | Pretrained zero-shot time-series forecasting model | Pre-trained language-model-based time-series forecaster |
| 원전≠ | Auer, A., Podest, P., Klotz, D., Böck, S., Klambauer, G., & Hochreiter, S. (2025). TiRex: Zero-shot forecasting across long and short horizons with enhanced in-context learning. arXiv preprint. 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 ↗ |
| 별칭 | Time-series xLSTM Forecaster, TiRex Zero-Shot, xLSTM Time-Series Model, Zaman Serisi Sıfır-Atım Tahmincisi | Chronos Forecasting Model, Amazon Chronos, Tokenized Time-Series LLM, Kronos Zaman Serisi Modeli |
| 관련≠ | 3 | 2 |
| 요약≠ | TiRex is a pretrained zero-shot time-series forecasting model introduced in 2025 by the NX-AI xLSTM team (Auer et al.). Built on the Extended Long Short-Term Memory (xLSTM) architecture, TiRex is trained at scale on diverse time-series corpora and can forecast unseen datasets without any fine-tuning. Its core idea is to exploit enhanced in-context learning: the model reads the entire available history as a context and produces forecasts for both short and long horizons directly from that context. | 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. |
| ScholarGate데이터셋 ↗ |
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