Bandingkan kaedah

	Autoformer: Penghuraian Transformer untuk Ramalan Deret Masa Jangka Panjang	Model ARIMA (Autoregresif Bersepadu Purata Bergerak)	TimesNet: Pemodelan Variasi Temporal 2D untuk Siri Masa
Bidang	Pembelajaran Mendalam	Ekonometrik	Pembelajaran Mendalam
Keluarga	Machine learning	Regression model	Machine learning
Tahun asal	2021	2015	2023
Pengasas	Haixu Wu et al. (Tsinghua)	Box & Jenkins (Box-Jenkins methodology)	Haixu Wu et al.
Jenis	Decomposition-based deep forecasting model	Univariate time-series model	2D convolutional time-series model
Sumber perintis	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	Wu, H., Hu, T., Liu, Y., Zhou, H., Wang, J., & Long, M. (2023). TimesNet: Temporal 2D-variation modeling for general time series analysis. ICLR.
Alias	Auto-Correlation Transformer, Decomposition Transformer, Series Decomposition Forecaster, Oto-Korelasyon Ayrışım Transformer	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	Temporal 2D-Variation Network, TimesNet Model, 2D Time-Series Network, Zamansal 2B Varyasyon Ağı
Berkaitan	4	5	2
Ringkasan	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).	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.