Should We Reconsider RNNs for Time-Series Forecasting?
(1) Background: In recent years, Transformer-based models have dominated the time-series forecasting domain, overshadowing recurrent neural networks (RNNs) such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). While Transformers demonstrate superior performance, their high computatio...
Saved in:
| Main Authors: | Vahid Naghashi, Mounir Boukadoum, Abdoulaye Banire Diallo |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
|
| Series: | AI |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-2688/6/5/90 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A multiscale model for multivariate time series forecasting
by: Vahid Naghashi, et al.
Published: (2025-01-01) -
Deep learning in time series forecasting with transformer models and RNNs
by: Rogerio Pereira dos Santos, et al.
Published: (2025-07-01) -
Data-Driven Optimized Load Forecasting: An LSTM-Based RNN Approach for Smart Grids
by: Muhammad Asghar Majeed, et al.
Published: (2025-01-01) -
Ultra-short-term Multi-region Power Load Forecasting Based on Spearman-GCN-GRU Model
by: Junying WU, et al.
Published: (2024-06-01) -
Force Control of SMA Springs Using RNNs
by: Ahmet Atasoy, et al.
Published: (2025-01-01)