Σύγκριση μεθόδων

	Autoformer: Transformer Αποσύνθεσης για Μακροχρόνιες Προβλέψεις Χρονοσειρών	Μοντέλο ARIMA (Autoregressive Integrated Moving Average)
Πεδίο	Βαθιά Μάθηση	Οικονομετρία
Οικογένεια	Machine learning	Regression model
Έτος προέλευσης	2021	2015
Δημιουργός	Haixu Wu et al. (Tsinghua)	Box & Jenkins (Box-Jenkins methodology)
Τύπος	Decomposition-based deep forecasting model	Univariate time-series model
Θεμελιώδης πηγή	Wu, H., Xu, J., Wang, J., & Long, M. (2021). Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting. NeurIPS, 34.	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	Auto-Correlation Transformer, Decomposition Transformer, Series Decomposition Forecaster, Oto-Korelasyon Ayrışım Transformer	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli
Συναφείς	4	5
Σύνοψη	Autoformer is a deep learning architecture for long-term time-series forecasting, introduced by Wu et al. from Tsinghua University at NeurIPS 2021. It replaces the standard self-attention mechanism with an Auto-Correlation mechanism that exploits periodic dependencies in the frequency domain, and embeds a progressive series decomposition block throughout the encoder and decoder to separately model trend and seasonal components.	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).