So sánh phương pháp

	LightTS: MLP lấy mẫu nhẹ cho dự báo chuỗi thời gian đa biến	TSMixer: Kiến trúc Toàn MLP cho Dự báo Chuỗi Thời gian
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2022	2023
Người khởi xướng	Tianping Zhang et al.	Si-An Chen et al. (Google)
Loại	Lightweight MLP-based multivariate time-series forecaster	All-MLP multivariate time-series forecasting model
Công trình gốc	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.	Chen, S.-A., Li, C.-L., Yoder, N., Arik, S. O., & Pfister, T. (2023). TSMixer: An all-MLP architecture for time series forecasting. Transactions on Machine Learning Research.
Tên gọi khác	Light Sampling-oriented MLP, LightMLP, Hafif Örnekleme Tabanlı MLP, Lightweight Time-Series MLP	All-MLP Time Series Mixer, Time Series Mixer, TSMixer Forecasting Model, Zaman Serisi Karıştırıcı
Liên quan	3	3
Tóm tắt	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.	TSMixer is a multivariate time-series forecasting model introduced by Si-An Chen and colleagues at Google in 2023. It challenges the prevailing dominance of Transformer-based architectures by demonstrating that a simple stack of interleaved MLP layers — alternating between mixing along the time axis and mixing across feature channels — achieves strong forecasting accuracy while remaining computationally efficient and easy to interpret architecturally.