השוואת שיטות

	SegRNN: רשת נוירונים רקורסיבית מבוססת מקטעים לחיזוי סדרות עתיות ארוכות טווח	LSTM
תחום	למידה עמוקה	למידה עמוקה
משפחה	Machine learning	Machine learning
שנת המקור	2023	1997
הוגה השיטה	Shengsheng Lin et al.	Hochreiter, S. & Schmidhuber, J.
סוג	Segment-based recurrent forecasting model	Recurrent neural network (gated memory cell)
מקור מכונן	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.
כינויים	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
קשורות	3	5
תקציר	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.