Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| FEDformer: Frequency Enhanced Decomposed Transformer× | TSMixer: Volledig MLP-architectuur voor tijdreeksvoorspelling× | |
|---|---|---|
| Vakgebied | Deep learning | Deep learning |
| Familie | Machine learning | Machine learning |
| Jaar van ontstaan≠ | 2022 | 2023 |
| Grondlegger≠ | Tian Zhou et al. | Si-An Chen et al. (Google) |
| Type≠ | Frequency-domain decomposed Transformer for time-series forecasting | All-MLP multivariate time-series forecasting model |
| Oorspronkelijke bron≠ | Zhou, T., Ma, Z., Wen, Q., Wang, X., Sun, L., & Jin, R. (2022). FEDformer: Frequency enhanced decomposed transformer for long-term series forecasting. ICML. link ↗ | 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. link ↗ |
| Aliassen | Frequency Enhanced Decomposed Transformer, FED-Transformer, Frequency Domain Transformer, Frekans Tabanlı Ayrıştırılmış Dönüştürücü | All-MLP Time Series Mixer, Time Series Mixer, TSMixer Forecasting Model, Zaman Serisi Karıştırıcı |
| Verwant | 3 | 3 |
| Samenvatting≠ | FEDformer is a Transformer-based architecture for long-term multivariate time-series forecasting, introduced by Zhou et al. at ICML 2022. Its core innovation is the combination of seasonal-trend decomposition with frequency-domain attention: instead of computing full token-to-token attention in the time domain, FEDformer projects queries, keys, and values into the frequency domain via Fourier or wavelet transforms and operates on a randomly selected subset of frequency components, achieving linear complexity while preserving global temporal structure. | 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. |
| ScholarGateGegevensset ↗ |
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