So sánh phương pháp

	TiRex: Dự báo chuỗi thời gian Zero-Shot với xLSTM	TimesFM: Mô hình nền tảng chỉ bộ giải mã cho dự báo chuỗi thời gian
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2025	2024
Người khởi xướng	NX-AI (xLSTM team)	Abhimanyu Das et al. (Google)
Loại	Pretrained zero-shot time-series forecasting model	Pre-trained decoder-only transformer for zero-shot time-series forecasting
Công trình gốc	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.	Das, A., Kong, W., Sen, R., & Zhou, Y. (2024). A decoder-only foundation model for time-series forecasting. ICML.
Tên gọi khác	Time-series xLSTM Forecaster, TiRex Zero-Shot, xLSTM Time-Series Model, Zaman Serisi Sıfır-Atım Tahmincisi	Time-series Foundation Model, Google TimesFM, TimesFM forecaster, Zaman Serisi Temel Modeli
Liên quan	3	3
Tóm tắt	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.	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.