Bandingkan metode

	Sundial: Model Dasar Deret Waktu Generatif	Moirai: Transformer Prediksi Deret Waktu Universal	TimesFM: Model Fondasi Hanya-Dekoder untuk Peramalan Deret Waktu
Bidang	Pembelajaran Mendalam	Pembelajaran Mendalam	Pembelajaran Mendalam
Keluarga	Machine learning	Machine learning	Machine learning
Tahun asal	2025	2024	2024
Pencetus	Yong Liu et al. (Tsinghua)	Gerald Woo et al. (Salesforce)	Abhimanyu Das et al. (Google)
Tipe	Generative time-series foundation model family	Foundation model for zero-shot time-series forecasting	Pre-trained decoder-only transformer for zero-shot time-series forecasting
Sumber perintis	Liu, Y., Qin, G., Shi, X., Hu, T., Wang, J., & Long, M. (2025). Sundial: A family of highly capable time series foundation models. ICML.	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.
Alias	Sundial TSF, Time-Series Foundation Model (Generative), Sundial ICML 2025, Zaman Serisi Temel Modeli (Sundial)	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
Terkait	3	3	3
Ringkasan	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.	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.