Compară metode

	LightTS: MLP cu eșantionare ușoară pentru prognoza seriilor de timp multivariate	DLinear: Model Linear de Descompunere pentru Prognoza Seriilor de Timp
Domeniu	Învățare profundă	Învățare profundă
Familie	Machine learning	Machine learning
Anul apariției	2022	2023
Autorul original	Tianping Zhang et al.	Ailing Zeng et al.
Tip	Lightweight MLP-based multivariate time-series forecaster	Decomposition-based linear forecasting model
Sursa seminală	Zhang, T., Zhang, Y., Cao, W., Bian, J., Yi, X., Zheng, S., & Li, J. (2022). Less is more: Fast multivariate time series forecasting with light sampling-oriented MLP structures. arXiv preprint.	Zeng, A., Chen, M., Zhang, L., & Xu, Q. (2023). Are transformers effective for time series forecasting? AAAI.
Denumiri alternative	Light Sampling-oriented MLP, LightMLP, Hafif Örnekleme Tabanlı MLP, Lightweight Time-Series MLP	Decomposition Linear, DLinear Forecaster, Linear Decomposition Model, Ayrışım Doğrusal Modeli
Înrudite	3	3
Rezumat	LightTS is a lightweight, MLP-based architecture for multivariate time-series forecasting introduced by Tianping Zhang and colleagues in 2022. Motivated by the observation that simpler models can match or surpass heavy Transformer-based architectures, LightTS applies an interval-sampling strategy to decompose long input sequences into multiple sub-sequences and processes each with compact Chunk-MLP and Continuous-MLP modules. The design prioritizes computational efficiency while preserving both local and global temporal patterns.	DLinear is a lightweight time series forecasting model introduced by Zeng et al. at AAAI 2023. It challenges the prevailing assumption that Transformer-based architectures are necessary for accurate long-horizon forecasting. The model decomposes an input sequence into trend and seasonal components using a moving average filter, then applies separate single-layer linear transformations to each component before summing their outputs to produce the final forecast.