विधियों की तुलना करें

	क्रोनोस: समय-श्रृंखला पूर्वानुमान के लिए एक टोकनाइज्ड फाउंडेशन मॉडल	मिश्रण विशेषज्ञ (Mixture of Experts)
क्षेत्र	गहन अधिगम	गहन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2024	2017
प्रवर्तक	Abdul Fatir Ansari et al. (Amazon)	Shazeer, N. et al.
प्रकार	Pre-trained language-model-based time-series forecaster	Sparse neural network architecture (conditional computation)
मौलिक स्रोत	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.	Shazeer, N. et al. (2017). Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer. ICLR. arXiv:1701.06538
उपनाम	Chronos Forecasting Model, Amazon Chronos, Tokenized Time-Series LLM, Kronos Zaman Serisi Modeli	Uzman Karışımı (Mixture of Experts — MoE), uzman karışımı, MoE, sparse mixture of experts
संबंधित	2	3
सारांश	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.	Mixture of Experts (MoE) is a sparse neural-network architecture, introduced by Shazeer and colleagues in 2017 with the sparsely-gated MoE layer, in which only a subset of expert sub-networks is activated for each input. As seen in models such as Switch Transformer and Mixtral, it holds computation cost fixed even as the total parameter count grows.