Improving time series forecasting using LSTM and attention models

By Hossein Abbasimehr, Reza Paki. Journal of Ambient Intelligence and Humanized Computing, 2021

298 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Signal Processing

View Paper: https://www.semanticscholar.org/paper/d144405244986d1647de041582d5b9e830d0e096

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Management Science and Operations Research

1. Muhammad Mohsin Waqas
2. Usa Wannasingha Humphries

In Signal Processing

1. Muhammad Mohsin Waqas
2. Usa Wannasingha Humphries

Citations in the corpus

• A critical review of RNN and LSTM variants in hydrological time series predictions (2024, background): “Due to their faster training times and reduced computational demands, GRUs are often more forgiving when it comes to hyperparameter selection [ 123 , 124 ].”