Making the whole greater than the sum of its parts: A literature review of ensemble methods for financial time series forecasting
This paper discusses the application of ensemble techniques for the prediction of time
series, presenting an in‐depth review of the main techniques and algorithms used by the …
series, presenting an in‐depth review of the main techniques and algorithms used by the …
Large-scale seasonal forecasts of river discharge by coupling local and global datasets with a stacked neural network: Case for the Loire River system
Accurate prediction of river discharge is critical for a wide range of sectors, from human
activities to environmental hazard management, especially in the face of increasing demand …
activities to environmental hazard management, especially in the face of increasing demand …
Time series prediction with neural networks: a review
VA Shterev, NS Metchkarski… - 2022 57th International …, 2022 - ieeexplore.ieee.org
One dimensional time series prediction is a major problem nowadays. These series can
describe physical phenomenon, traffic flow, economic transactions, etc. Anomaly detection …
describe physical phenomenon, traffic flow, economic transactions, etc. Anomaly detection …
Deep learning-based fault prediction in wireless sensor network embedded cyber-physical systems for industrial processes
This paper investigates the challenging fault prediction problem in process industries that
adopt autonomous and intelligent cyber-physical systems (CPS), which is in line with the …
adopt autonomous and intelligent cyber-physical systems (CPS), which is in line with the …
Navigating weight prediction with diet diary
Current research in food analysis primarily concentrates on tasks such as food recognition,
recipe retrieval and nutrition estimation from a single image. Nevertheless, there is a …
recipe retrieval and nutrition estimation from a single image. Nevertheless, there is a …
Predicting sedentary behavior in adults using stacked LSTM modeling
In recent periods, there has been a noticeable emergence of a new health concern
associated with uncertain sedentary behavior. Across all adult age groups, prolonged …
associated with uncertain sedentary behavior. Across all adult age groups, prolonged …
CL-MFGCN: Graph Structure Contrastive Learning and Multi-Scale Feature Fusion Graph Convolutional Network for Spectrum Prediction
To address the conflict between the limited availability of spectrum resources and the swiftly
growing number of frequency equipment in the Internet of Vehicles, this paper starts from the …
growing number of frequency equipment in the Internet of Vehicles, this paper starts from the …
Deep mobile path prediction with shift-and-join and carry-ahead
Importance of user mobility has rapidly increased in 5G due to reduced cell sizes,
management of Multi-access Edge Computing (MEC), and ultra-low latency services …
management of Multi-access Edge Computing (MEC), and ultra-low latency services …
Deep learning-driven Mie scattering prediction method for radially varying spherical particles
G Wang, Z Li, C Hu, G Yang, X Yang, B Liu - Optics & Laser Technology, 2024 - Elsevier
Efficient and accurate calculation of Mie scattering parameters for aerosol particles holds
significant scientific value and practical implications across various fields such as climate …
significant scientific value and practical implications across various fields such as climate …
Modelling of GNSS station position time series using deep learning approaches
M Şimşek, M Taşkıran, U Doğan - Earth Science Informatics, 2025 - Springer
Abstract GNSS (Global Navigation Satellite System) time series are indispensable in
geodesy, geophysics, and other Earth sciences, and serve as important tools for monitoring …
geodesy, geophysics, and other Earth sciences, and serve as important tools for monitoring …