Σύγκριση μεθόδων

	PatchTST	Μοντέλο ARIMA (Autoregressive Integrated Moving Average)
Πεδίο	Βαθιά Μάθηση	Οικονομετρία
Οικογένεια	Machine learning	Regression model
Έτος προέλευσης	2023	2015
Δημιουργός	Nie, Y. et al.	Box & Jenkins (Box-Jenkins methodology)
Τύπος	Transformer for time series forecasting	Univariate time-series model
Θεμελιώδης πηγή	Nie, Y., Nguyen, N. H., Sinthong, P. & Kalagnanam, J. (2023). A Time Series is Worth 64 Words: Long-term Forecasting with Transformers. ICLR.	Box, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021
Εναλλακτικές ονομασίες	PatchTST — Yama Tabanlı Zaman Serisi Transformer, patch-based time series transformer, channel-independent transformer	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli
Συναφείς	3	5
Σύνοψη	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.	ARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed., 2015).