পদ্ধতির তুলনা করুন

	TSMixer: সময় সিরিজের পূর্বাভাসের জন্য সম্পূর্ণ MLP আর্কিটেকচার	সময় সিরিজ পূর্বাভাসের জন্য DLinear: ডিকম্পোজিশন লিনিয়ার মডেল	মাল্টিলেয়ার পারসেপ্ট্রন (MLP)
ক্ষেত্র	গভীর শিখন	গভীর শিখন	গভীর শিখন
পরিবার	Machine learning	Machine learning	Machine learning
উদ্ভবের বছর	2023	2023	1986
প্রবর্তক	Si-An Chen et al. (Google)	Ailing Zeng et al.	Rumelhart, D. E.; Hinton, G. E.; Williams, R. J.
ধরন	All-MLP multivariate time-series forecasting model	Decomposition-based linear forecasting model	Supervised feedforward neural network
মৌলিক উৎস	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.	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.
অপর নাম	All-MLP Time Series Mixer, Time Series Mixer, TSMixer Forecasting Model, Zaman Serisi Karıştırıcı	Decomposition Linear, DLinear Forecaster, Linear Decomposition Model, Ayrışım Doğrusal Modeli	MLP, feedforward neural network, fully connected neural network, vanilla neural network
সম্পর্কিত	3	3	4
সারসংক্ষেপ	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.	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.