A combined model based on recurrent neural networks and graph convolutional networks for financial time series forecasting
Accurate and real-time forecasting of the price of oil plays an important role in the world
economy. Research interest in forecasting this type of time series has increased …
economy. Research interest in forecasting this type of time series has increased …
A novel sequence to sequence data modelling based CNN-LSTM algorithm for three years ahead monthly peak load forecasting
Long-term load forecasting (LTLF) models play an important role in the strategic planning of
power systems around the globe. Obtaining correct decisions on power network expansions …
power systems around the globe. Obtaining correct decisions on power network expansions …
Building trend fuzzy granulation-based LSTM recurrent neural network for long-term time-series forecasting
Y Tang, F Yu, W Pedrycz, X Yang… - IEEE transactions on …, 2021 - ieeexplore.ieee.org
The existing long-term time-series forecasting methods based on the neural networks suffer
from multiple limitations, such as accumulated errors and diminishing temporal correlation …
from multiple limitations, such as accumulated errors and diminishing temporal correlation …
Deep learning-based time series forecasting
Time series forecasting methods have been widely implemented in various domains across
industry and academics. For decision-makers in the forecasting sector, decision processes …
industry and academics. For decision-makers in the forecasting sector, decision processes …
[HTML][HTML] Implementing recurrent neural networks in process systems engineering applications, the right way!
A Chandrasekar, T Wortley, E Bohm… - Computers & Chemical …, 2025 - Elsevier
This manuscript identifies, addresses and illustrates via comparisons an inconsistency and
inaccuracy with the implementation of Recurrent Neural Networks (RNNs) on naturally …
inaccuracy with the implementation of Recurrent Neural Networks (RNNs) on naturally …
Exploring the predictability of range‐based volatility estimators using recurrent neural networks
G Petneházi, J Gáll - Intelligent Systems in Accounting, Finance …, 2019 - Wiley Online Library
We investigate the predictability of several range‐based stock volatility estimates and
compare them with the standard close‐to‐close estimate, which is most commonly …
compare them with the standard close‐to‐close estimate, which is most commonly …
Multi-period time series modeling with sparsity via Bayesian variational inference
D Hsu - arxiv preprint arxiv:1707.00666, 2017 - arxiv.org
In this paper, we use augmented the hierarchical latent variable model to model multi-period
time series, where the dynamics of time series are governed by factors or trends in multiple …
time series, where the dynamics of time series are governed by factors or trends in multiple …
Ac-lstm: Anomaly state perception of infrared point targets based on cnn+ lstm
J Sun, J Wang, Z Hao, M Zhu, H Sun, M Wei, K Dong - Remote Sensing, 2022 - mdpi.com
Anomaly perception of infrared point targets has high application value in many fields, such
as maritime surveillance, airspace surveillance, and early warning systems. This kind of …
as maritime surveillance, airspace surveillance, and early warning systems. This kind of …
Multi-step-ahead prediction with long short term memory networks and support vector regression
S Li, H Fang, B Shi - 2018 37th Chinese Control Conference …, 2018 - ieeexplore.ieee.org
Long-term machine condition prognosis is very important for condition-based maintenance.
With the rapid development of deep learning, we use long short term memory (LSTM) …
With the rapid development of deep learning, we use long short term memory (LSTM) …
Multi-task learning and attention mechanism based long short-term memory for temperature prediction of EMU bearing
Y Chen, C Zhang, N Zhang, Y Chen… - 2019 Prognostics and …, 2019 - ieeexplore.ieee.org
The traction motor is one of the key components that plays an important role in ensuring the
safety and stability of the running EMU (Electric Multiple Units). The running state of the …
safety and stability of the running EMU (Electric Multiple Units). The running state of the …