MAP-FCRNN: Multi-step ahead prediction model using forecasting correction and RNN model with memory functions-Reference-Cited by-同舟云学术

MAP-FCRNN: Multi-step ahead prediction model using forecasting correction and RNN model with memory functions

Published:2023-10 Issue: Volume:646 Page:119382
ISSN:0020-0255
Container-title:Information Sciences
language:en
Short-container-title:Information Sciences

Author:

Zhang Rongtao,Ma Xueling^ORCID,Ding Weiping^ORCID,Zhan Jianming^ORCID

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Computer Science Applications,Theoretical Computer Science,Control and Systems Engineering,Software

Reference50 articles.

1. A review on machine learning forecasting growth trends and their real–time applications in different energy systems;Ahmad;Sustain. Cities Soc.,2020

2. Forecasting the dynamics of cumulative COVID-19 cases (confirmed, recovered and deaths) for top-16 countries using statistical machine learning models: Auto-Regressive Integrated Moving Average (ARIMA) and Seasonal Auto-Regressive Integrated Moving Average (SARIMA)

3. Adaptive grid-based forest-like clustering algorithm;Cheng;Neurocomputing,2022

4. Fair algorithms for hierarchical agglomerative clustering;Chhabra,2022

5. ForecastNet: a time-variant deep feed-forward neural network architecture for multi–step–ahead time–series forecasting;Dabrowski,2020

Cited by 9 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Design of a bi-level PSO based modular neural network for multi-step time series prediction;Applied Intelligence;2024-07-01

2. MEAformer: An all-MLP transformer with temporal external attention for long-term time series forecasting;Information Sciences;2024-05

3. GA-FCFNN: A new forecasting method combining feature selection methods and feedforward neural networks using genetic algorithms;Information Sciences;2024-05

4. A local multi-granularity fuzzy rough set method for multi-attribute decision making based on MOSSO-LSTM and its application in stock market;Applied Intelligence;2024-04

5. Bayesian network based probabilistic weighted high-order fuzzy time series forecasting;Expert Systems with Applications;2024-03