เปรียบเทียบวิธี

	แบบจำลอง ARIMA (Autoregressive Integrated Moving Average)	FEDformer: หม้อแปลงไฟฟ้าแบบแยกส่วนที่ปรับปรุงความถี่
สาขาวิชา	เศรษฐมิติ	การเรียนรู้เชิงลึก
ตระกูล	Regression model	Machine learning
ปีกำเนิด	2015	2022
ผู้ริเริ่ม	Box & Jenkins (Box-Jenkins methodology)	Tian Zhou et al.
ประเภท	Univariate time-series model	Frequency-domain decomposed Transformer for time-series forecasting
แหล่งต้นตำรับ	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	Zhou, T., Ma, Z., Wen, Q., Wang, X., Sun, L., & Jin, R. (2022). FEDformer: Frequency enhanced decomposed transformer for long-term series forecasting. ICML.
ชื่อเรียกอื่น	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	Frequency Enhanced Decomposed Transformer, FED-Transformer, Frequency Domain Transformer, Frekans Tabanlı Ayrıştırılmış Dönüştürücü
ที่เกี่ยวข้อง	5	3
สรุป	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).	FEDformer is a Transformer-based architecture for long-term multivariate time-series forecasting, introduced by Zhou et al. at ICML 2022. Its core innovation is the combination of seasonal-trend decomposition with frequency-domain attention: instead of computing full token-to-token attention in the time domain, FEDformer projects queries, keys, and values into the frequency domain via Fourier or wavelet transforms and operates on a randomly selected subset of frequency components, achieving linear complexity while preserving global temporal structure.