Módszerek összehasonlítása

	Chronos: Tokenizált Alapmodell Idősor-előrejelzéshez	Moirai: Univerzális idősor-előrejelző Transformer	TimesFM: Alapmodell dekóder-only architektúrával idősor-előrejelzéshez
Tudományterület	Mélytanulás	Mélytanulás	Mélytanulás
Módszercsalád	Machine learning	Machine learning	Machine learning
Keletkezés éve	2024	2024	2024
Megalkotó	Abdul Fatir Ansari et al. (Amazon)	Gerald Woo et al. (Salesforce)	Abhimanyu Das et al. (Google)
Típus	Pre-trained language-model-based time-series forecaster	Foundation model for zero-shot time-series forecasting	Pre-trained decoder-only transformer for zero-shot time-series forecasting
Alapmű	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.	Woo, G., Liu, C., Kumar, A., Xiong, C., Savarese, S., & Sahoo, D. (2024). Unified training of universal time series forecasting transformers. ICML.	Das, A., Kong, W., Sen, R., & Zhou, Y. (2024). A decoder-only foundation model for time-series forecasting. ICML.
Alternatív nevek	Chronos Forecasting Model, Amazon Chronos, Tokenized Time-Series LLM, Kronos Zaman Serisi Modeli	Unified Time-Series Transformer, Universal Forecasting Transformer, MOIRAI, Evrensel Zaman Serisi Tahmin Transformatörü	Time-series Foundation Model, Google TimesFM, TimesFM forecaster, Zaman Serisi Temel Modeli
Kapcsolódó	2	3	3
Összefoglaló	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.	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.	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.