مقایسهٔ روشها
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| مویرای: ترانسفورمر جهانی پیشبینی سریهای زمانی× | کرونوس: یک مدل بنیادی توکنسازیشده برای پیشبینی سریهای زمانی× | |
|---|---|---|
| حوزه | یادگیری عمیق | یادگیری عمیق |
| خانواده | Machine learning | Machine learning |
| سال پیدایش | 2024 | 2024 |
| پدیدآور≠ | Gerald Woo et al. (Salesforce) | Abdul Fatir Ansari et al. (Amazon) |
| نوع≠ | Foundation model for zero-shot time-series forecasting | Pre-trained language-model-based time-series forecaster |
| منبع بنیادین≠ | Woo, G., Liu, C., Kumar, A., Xiong, C., Savarese, S., & Sahoo, D. (2024). Unified training of universal time series forecasting transformers. 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 ↗ |
| نامهای دیگر | Unified Time-Series Transformer, Universal Forecasting Transformer, MOIRAI, Evrensel Zaman Serisi Tahmin Transformatörü | Chronos Forecasting Model, Amazon Chronos, Tokenized Time-Series LLM, Kronos Zaman Serisi Modeli |
| مرتبط≠ | 3 | 2 |
| خلاصه≠ | Moirai is a foundation model for universal time-series forecasting introduced by Gerald Woo and colleagues at Salesforce Research in 2024 and presented at ICML. The core idea is to pre-train a single large Transformer on an exceptionally diverse corpus of time-series data (LOTSA) spanning many domains and frequencies, enabling zero-shot and few-shot forecasting on unseen datasets without task-specific retraining. Moirai employs patch-based tokenization, any-variate attention, and a mixture-of-distributions output head to handle variable frequencies, multiple variates, and probabilistic prediction in a unified architecture. | 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. |
| ScholarGateمجموعهداده ↗ |
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