Usporedite metode

	TiDE: Vremenski gusto zgušnjavanje	DLinear: Dekompozicijski linearni model za prognoziranje vremenskih serija	TSMixer
Područje	Duboko učenje	Duboko učenje	Duboko učenje
Obitelj	Machine learning	Machine learning	Machine learning
Godina nastanka	2023	2023	2023
Tvorac	Abhimanyu Das et al.	Ailing Zeng et al.	Si-An Chen et al. (Google)
Vrsta	MLP-based encoder-decoder for long-term time-series forecasting	Decomposition-based linear forecasting model	All-MLP multivariate time-series forecasting model
Temeljni izvor	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.	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.
Drugi nazivi	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	All-MLP Time Series Mixer, Time Series Mixer, TSMixer Forecasting Model, Zaman Serisi Karıştırıcı
Srodne	3	3	3
Sažetak	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.	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.