Deep learning based long-term global solar irradiance and temperature forecasting using time series with multi-step multivariate output
Solar radiation's intermittent and fluctuating nature poses severe limitations for most
applications. Accurate prediction of solar radiation is an essential factor in predicting the …
applications. Accurate prediction of solar radiation is an essential factor in predicting the …
[HTML][HTML] GBT: Two-stage transformer framework for non-stationary time series forecasting
L Shen, Y Wei, Y Wang - Neural Networks, 2023 - Elsevier
This paper shows that time series forecasting Transformer (TSFT) suffers from severe over-
fitting problem caused by improper initialization method of unknown decoder inputs …
fitting problem caused by improper initialization method of unknown decoder inputs …
A novel smoothing-based deep learning time-series approach for daily suspended sediment load prediction
Precise assessment of suspended sediment load (SSL) is vital for many applications in
hydrological modeling and hydraulic engineering. In this study, a smoothed long short-term …
hydrological modeling and hydraulic engineering. In this study, a smoothed long short-term …
[HTML][HTML] Hydrological drought forecasting and monitoring system development using artificial neural network (ANN) in Ethiopia
KA Tareke, AG Awoke - Heliyon, 2023 - cell.com
The objective of this study is to investigate and perform long–term forecasting of both
streamflow and hydrological drought over Ethiopia. Observed streamflow and precipitation …
streamflow and hydrological drought over Ethiopia. Observed streamflow and precipitation …
Bank Soundness Level Prediction: ANFIS vs Deep Learning
SN Maharani, B Sugeng… - Journal of Applied …, 2023 - bright-journal.org
The systemic nature of the risk of bankruptcy of financial institutions has become an
important issue in maintaining the existence and stability of domestic and global finance …
important issue in maintaining the existence and stability of domestic and global finance …
A shale gas production prediction model based on masked convolutional neural network
W Zhou, X Li, ZL Qi, HH Zhao, J Yi - Applied Energy, 2024 - Elsevier
Shale gas production prediction is of great significance for shale gas exploration and
development, as it can optimize exploration strategies and guide adjustments to production …
development, as it can optimize exploration strategies and guide adjustments to production …
Multimodal 1D CNN for delamination prediction in CFRP drilling process with industrial robots
There is a growing demand for carbon fiber-reinforced plastics (CFRPs) in the aerospace
and automotive industries. Consequently, the assembly and repair of CFRP components …
and automotive industries. Consequently, the assembly and repair of CFRP components …
[HTML][HTML] Comparative Analysis of Convolutional Neural Network-Long Short-Term Memory, Sparrow Search Algorithm-Backpropagation Neural Network, and Particle …
L Zhen, A Bărbulescu - Water, 2024 - mdpi.com
Modeling and forecasting the river flow is essential for the management of water resources.
In this study, we conduct a comprehensive comparative analysis of different models built for …
In this study, we conduct a comprehensive comparative analysis of different models built for …
Service-oriented model-based fault prediction and localization for service compositions testing using deep learning techniques
As service-oriented computing systems become more buoyant and complex, the occurrence
of faults dramatically increases. Fault prediction plays a crucial role in the service-oriented …
of faults dramatically increases. Fault prediction plays a crucial role in the service-oriented …
Deep learning approaches for MIMO time-series analysis
F Kurniawan, S Sulaiman… - … of Advances in …, 2023 - repository.uin-malang.ac.id
This study presents a comparative analysis of various deep learning (DL) methods for multi-
input and multi-output (MIMO) time-series forecasting of stock prices. The analysis is …
input and multi-output (MIMO) time-series forecasting of stock prices. The analysis is …