Jämför metoder

	LightTS	DLinear: Decomposition Linear Model för tidsserieprognoser	Multilayer Perceptron (MLP)	TSMixer: All-MLP-arkitektur för tidsserieprognoser
Ämnesområde	Djupinlärning	Djupinlärning	Djupinlärning	Djupinlärning
Familj	Machine learning	Machine learning	Machine learning	Machine learning
Ursprungsår	2022	2023	1986	2023
Upphovsperson	Tianping Zhang et al.	Ailing Zeng et al.	Rumelhart, D. E.; Hinton, G. E.; Williams, R. J.	Si-An Chen et al. (Google)
Typ	Lightweight MLP-based multivariate time-series forecaster	Decomposition-based linear forecasting model	Supervised feedforward neural network	All-MLP multivariate time-series forecasting model
Ursprungskälla	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.	Rumelhart, D. E., Hinton, G. E. & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323, 533–536.	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.
Alias	Light Sampling-oriented MLP, LightMLP, Hafif Örnekleme Tabanlı MLP, Lightweight Time-Series MLP	Decomposition Linear, DLinear Forecaster, Linear Decomposition Model, Ayrışım Doğrusal Modeli	MLP, feedforward neural network, fully connected neural network, vanilla neural network	All-MLP Time Series Mixer, Time Series Mixer, TSMixer Forecasting Model, Zaman Serisi Karıştırıcı
Närliggande	3	3	4	3
Sammanfattning	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.	A Multilayer Perceptron is a classic fully connected feedforward neural network trained with the backpropagation algorithm, as formalised by Rumelhart, Hinton & Williams in their landmark 1986 Nature paper. Composed of an input layer, one or more hidden layers of neurons, and an output layer, the MLP learns nonlinear mappings from input features to target outputs and serves as the foundational building block of modern deep learning.	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.