مقایسهٔ روش‌ها

	TiRex: پیش‌بینی سری‌های زمانی با یادگیری صفر-شات با استفاده از xLSTM	LSTM
حوزه	یادگیری عمیق	یادگیری عمیق
خانواده	Machine learning	Machine learning
سال پیدایش	2025	1997
پدیدآور	NX-AI (xLSTM team)	Hochreiter, S. & Schmidhuber, J.
نوع	Pretrained zero-shot time-series forecasting model	Recurrent neural network (gated memory cell)
منبع بنیادین	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.	Hochreiter, S. & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780.
نام‌های دیگر	Time-series xLSTM Forecaster, TiRex Zero-Shot, xLSTM Time-Series Model, Zaman Serisi Sıfır-Atım Tahmincisi	LSTM (Uzun Kısa Dönem Bellek Ağı), long short-term memory, LSTM network, recurrent neural network with memory cells
مرتبط	3	5
خلاصه	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.	LSTM (Long Short-Term Memory) is a recurrent neural network architecture, introduced by Sepp Hochreiter and Jürgen Schmidhuber in 1997, that can learn long-term dependencies in sequential data and is widely used for time-series and sequence prediction. It keeps an internal memory that lets information persist across many time steps.