Salīdzināt metodes

	SCINet: paraugu konvolūciju un mijiedarbības tīkls laika virkņu prognozēšanai	TimesNet: La laika datu 2D-variāciju modelēšana
Nozare	Dziļā mācīšanās	Dziļā mācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2022	2023
Autors	Minhao Liu et al.	Haixu Wu et al.
Tips	Hierarchical convolutional time-series forecasting network	2D convolutional time-series model
Pirmavots	Liu, M., Zeng, A., Chen, M., Xu, Z., Lai, Q., Ma, L., & Xu, Q. (2022). SCINet: Time series modeling and forecasting with sample convolution and interaction. NeurIPS.	Wu, H., Hu, T., Liu, Y., Zhou, H., Wang, J., & Long, M. (2023). TimesNet: Temporal 2D-variation modeling for general time series analysis. ICLR.
Citi nosaukumi	Sample Convolution and Interaction Network, SCI-Net, Temporal Downsampling Convolution Network, Örneklem Evrişim ve Etkileşim Ağı	Temporal 2D-Variation Network, TimesNet Model, 2D Time-Series Network, Zamansal 2B Varyasyon Ağı
Saistītās	2	2
Kopsavilkums	SCINet is a deep learning architecture for multi-step time-series forecasting introduced by Liu et al. at NeurIPS 2022. Its core idea is a recursive binary-tree structure of SCI-Blocks, each of which splits an input sequence into odd- and even-indexed sub-sequences, applies convolutional filters to model cross-subsequence interactions, and then merges the learned representations. This hierarchical downsampling strategy enables the network to capture temporal dependencies at multiple resolutions simultaneously.	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.