Vertaile menetelmiä

	SegRNN: Segment Recurrent Neural Network pitkän aikavälin aikasarjaennustamiseen	LSTM
Tieteenala	Syväoppiminen	Syväoppiminen
Menetelmäperhe	Machine learning	Machine learning
Syntyvuosi	2023	1997
Kehittäjä	Shengsheng Lin et al.	Hochreiter, S. & Schmidhuber, J.
Tyyppi	Segment-based recurrent forecasting model	Recurrent neural network (gated memory cell)
Alkuperäislähde	Lin, S., Lin, W., Wu, W., Zhao, F., Mo, R., & Zhang, H. (2023). SegRNN: Segment recurrent neural network for long-term time series forecasting. arXiv preprint.	Hochreiter, S. & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780.
Rinnakkaisnimet	Segment RNN, Segment Recurrent Neural Network, SegRNN forecaster, Bölümlü Tekrarlayan Sinir Ağı	LSTM (Uzun Kısa Dönem Bellek Ağı), long short-term memory, LSTM network, recurrent neural network with memory cells
Liittyvät	3	5
Tiivistelmä	SegRNN is a recurrent neural network architecture for long-term time series forecasting proposed by Shengsheng Lin et al. in 2023. Instead of processing one time step at a time, SegRNN partitions input sequences into fixed-length segments and feeds each segment as a single token into a GRU. This segment-based design drastically reduces the number of recurrent iterations, addressing the well-known difficulty RNNs face when modeling very long dependencies over many individual steps.	LSTM (Long Short-Term Memory) is a recurrent neural network architecture, introduced by Sepp Hochreiter and Jürgen Schmidhuber in 1997, that can learn long-term dependencies in sequential data and is widely used for time-series and sequence prediction. It keeps an internal memory that lets information persist across many time steps.