Módszerek összehasonlítása

	FreTS: Frekvenciatartománybeli MLP-k idősor-előrejelzéshez	FiLM: Frekvenciaoptimalizált Legendre Memória Modell	TSMixer: Minden-MLP Architektúra Idősor-Előrejelzéshez
Tudományterület	Mélytanulás	Mélytanulás	Mélytanulás
Módszercsalád	Machine learning	Machine learning	Machine learning
Keletkezés éve	2023	2022	2023
Megalkotó	Kun Yi et al.	Tian Zhou et al.	Si-An Chen et al. (Google)
Típus	Frequency-domain MLP forecasting model	Frequency-domain time-series forecasting model	All-MLP multivariate time-series forecasting model
Alapmű	Yi, K., Zhang, Q., Fan, W., Wang, S., Wang, P., He, H., An, N., Lian, D., Cao, L., & Niu, Z. (2023). Frequency-domain MLPs are more effective learners in time series forecasting. NeurIPS.	Zhou, T., Ma, Z., Wen, Q., Sun, L., Yao, T., Yin, W., & Jin, R. (2022). FiLM: Frequency improved Legendre memory model for long-term time series forecasting. NeurIPS.	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.
Alternatív nevek	Frequency-domain MLPs, FrequencyMLP, FreTS Forecaster, Frekans Alanı MLP	Frequency Improved Legendre Memory, FiLM Forecaster, Legendre Frequency Model, Frekans Tabanlı Legendre Bellek Modeli	All-MLP Time Series Mixer, Time Series Mixer, TSMixer Forecasting Model, Zaman Serisi Karıştırıcı
Kapcsolódó	3	3	3
Összefoglaló	FreTS is a time series forecasting architecture introduced by Yi et al. at NeurIPS 2023. It departs from Transformer-based designs by applying simple Multi-Layer Perceptrons (MLPs) entirely in the frequency domain. The model transforms input sequences with the Discrete Fourier Transform and then learns temporal and channel dependencies through complex-valued MLP layers, achieving competitive or superior long-term forecasting accuracy with substantially lower computational cost.	FiLM is a long-term time-series forecasting architecture introduced by Tian Zhou and colleagues at NeurIPS 2022. It combines Legendre polynomial projections of the historical input with learnable frequency-domain filters applied to the resulting coefficient sequences. By representing history as a compact set of polynomial coefficients and filtering those coefficients in the frequency domain, FiLM enables efficient extrapolation over long prediction horizons without the quadratic cost of full self-attention.	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.