विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| Sundial: जनरेटिव टाइम-सीरीज़ फ़ाउंडेशन मॉडल्स× | क्रोनोस: समय-श्रृंखला पूर्वानुमान के लिए एक टोकनाइज्ड फाउंडेशन मॉडल× | |
|---|---|---|
| क्षेत्र | गहन अधिगम | गहन अधिगम |
| परिवार | Machine learning | Machine learning |
| उद्भव वर्ष≠ | 2025 | 2024 |
| प्रवर्तक≠ | Yong Liu et al. (Tsinghua) | Abdul Fatir Ansari et al. (Amazon) |
| प्रकार≠ | Generative time-series foundation model family | Pre-trained language-model-based time-series forecaster |
| मौलिक स्रोत≠ | Liu, Y., Qin, G., Shi, X., Hu, T., Wang, J., & Long, M. (2025). Sundial: A family of highly capable time series foundation models. 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 ↗ |
| उपनाम | Sundial TSF, Time-Series Foundation Model (Generative), Sundial ICML 2025, Zaman Serisi Temel Modeli (Sundial) | Chronos Forecasting Model, Amazon Chronos, Tokenized Time-Series LLM, Kronos Zaman Serisi Modeli |
| संबंधित≠ | 3 | 2 |
| सारांश≠ | Sundial is a family of generative time-series foundation models introduced by Yong Liu and colleagues at Tsinghua University (ICML 2025). Pre-trained on large and diverse time-series corpora, Sundial employs a decomposition-based architecture paired with a generative forecasting head to produce probabilistic multi-horizon forecasts. It represents a shift toward general-purpose, zero-shot-capable models for real-world temporal prediction tasks. | 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. |
| ScholarGateडेटासेट ↗ |
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