Võrdle meetodeid

	TiDE: Tihe kodeerija ajasarjade jaoks	DLinear: Dekompositsiooniline lineaarne mudel aegridade prognoosimiseks	Multilayer Perceptron (MLP)	TSMixer: Kõik-MLP arhitektuur aegridade prognoosimiseks
Valdkond	Süvaõpe	Süvaõpe	Süvaõpe	Süvaõpe
Perekond	Machine learning	Machine learning	Machine learning	Machine learning
Tekkeaasta	2023	2023	1986	2023
Looja	Abhimanyu Das et al.	Ailing Zeng et al.	Rumelhart, D. E.; Hinton, G. E.; Williams, R. J.	Si-An Chen et al. (Google)
Tüüp	MLP-based encoder-decoder for long-term time-series forecasting	Decomposition-based linear forecasting model	Supervised feedforward neural network	All-MLP multivariate time-series forecasting model
Algallikas	Das, A., Kong, W., Leach, A., Mathur, S., Sen, R., & Yu, R. (2023). Long-term forecasting with TiDE: Time-series dense encoder. Transactions on Machine Learning Research.	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.
Rööpnimetused	Time-series Dense Encoder, TiDE model, Dense Encoder for Long-term Forecasting, Yoğun Kodlayıcı Zaman Serisi Modeli	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
Seotud	3	3	4	3
Kokkuvõte	TiDE (Time-series Dense Encoder) is an MLP-based encoder-decoder architecture for long-term multivariate time-series forecasting, introduced by Abhimanyu Das and colleagues at Google Research in 2023. The model encodes past time-series observations together with static and dynamic covariates through stacked dense (MLP) layers, then decodes a latent representation into future forecasts. TiDE demonstrates that simple linear and dense architectures can match or outperform Transformer-based models on standard long-term forecasting benchmarks while being significantly faster.	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.