Linganisha mbinu

	Mfumo wa ARIMA (Autoregressive Integrated Moving Average)	FEDformer: Transformer Iliyoimarishwa kwa Masafa na Uozo	TimesNet: Uundaji wa Miundo Mikuu ya 2D ya Muda kwa Mfululizo wa Wakati
Nyanja	Ekonometriki	Ujifunzaji wa Kina	Ujifunzaji wa Kina
Familia	Regression model	Machine learning	Machine learning
Mwaka wa asili	2015	2022	2023
Mwanzilishi	Box & Jenkins (Box-Jenkins methodology)	Tian Zhou et al.	Haixu Wu et al.
Aina	Univariate time-series model	Frequency-domain decomposed Transformer for time-series forecasting	2D convolutional time-series model
Chanzo asilia	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.	Wu, H., Hu, T., Liu, Y., Zhou, H., Wang, J., & Long, M. (2023). TimesNet: Temporal 2D-variation modeling for general time series analysis. ICLR.
Majina mbadala	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	Frequency Enhanced Decomposed Transformer, FED-Transformer, Frequency Domain Transformer, Frekans Tabanlı Ayrıştırılmış Dönüştürücü	Temporal 2D-Variation Network, TimesNet Model, 2D Time-Series Network, Zamansal 2B Varyasyon Ağı
Zinazohusiana	5	3	2
Muhtasari	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.	TimesNet is a general-purpose time-series model introduced by Wu et al. at ICLR 2023. Its central idea is that univariate or multivariate time series can be reinterpreted as collections of two-dimensional temporal maps by reshaping the 1D signal according to its dominant periodicities, detected via Fast Fourier Transform. This 1D-to-2D transformation exposes both intraperiod patterns (within one cycle) and interperiod trends (across cycles), enabling powerful 2D convolutional architectures to model temporal variation.