Vertaile menetelmiä

	DLinear: Hajotelmaan perustuva lineaarinen malli aikasarjaennustukseen	Monikerki-kerrosperceptron (MLP)
Tieteenala	Syväoppiminen	Syväoppiminen
Menetelmäperhe	Machine learning	Machine learning
Syntyvuosi	2023	1986
Kehittäjä	Ailing Zeng et al.	Rumelhart, D. E.; Hinton, G. E.; Williams, R. J.
Tyyppi	Decomposition-based linear forecasting model	Supervised feedforward neural network
Alkuperäislähde	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.
Rinnakkaisnimet	Decomposition Linear, DLinear Forecaster, Linear Decomposition Model, Ayrışım Doğrusal Modeli	MLP, feedforward neural network, fully connected neural network, vanilla neural network
Liittyvät	3	4
Tiivistelmä	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.