Salīdzināt metodes

	PatchTST	Konformālā prognozēšana laika sēriju prognozēšanai
Nozare	Dziļā mācīšanās	Ekonometrija
Saime	Machine learning	Regression model
Izcelsmes gads	2023	2021
Autors	Nie, Y. et al.	Angelopoulos & Bates (tutorial); Xu & Xie (time-series EnbPI)
Tips	Transformer for time series forecasting	Distribution-free prediction interval wrapper
Pirmavots	Nie, Y., Nguyen, N. H., Sinthong, P. & Kalagnanam, J. (2023). A Time Series is Worth 64 Words: Long-term Forecasting with Transformers. ICLR.	Angelopoulos, A. N. & Bates, S. (2023). Conformal Prediction: A Gentle Introduction. Foundations and Trends in Machine Learning, 16(4), 494-591.
Citi nosaukumi	PatchTST — Yama Tabanlı Zaman Serisi Transformer, patch-based time series transformer, channel-independent transformer	conformal prediction, distribution-free prediction intervals, EnbPI, Konformal Tahmin (Conformal Prediction — Zaman Serisi)
Saistītās	3	4
Kopsavilkums	PatchTST is a patch-based Transformer architecture for time series forecasting, introduced by Nie and colleagues in 2023, that cuts each series into overlapping patches treated as tokens and processes channels independently. It balances computational efficiency with strong accuracy on long-horizon forecasting.	Conformal prediction is a distribution-free wrapper that turns any point forecaster — ARIMA, a neural network, or a machine-learning model — into valid prediction intervals using only its residuals. The time-series form was popularised by Xu & Xie (2021) and the modern tutorial treatment by Angelopoulos & Bates (2023).