Results 91 to 100 of about 38,147 (211)

Forecasting Airtel Stock Prices Through Decomposition and Integration: A Novel VMD-GARCH-LSTM Framework

International Journal of Mathematics and Mathematical Sciences
Stock price forecasting is complex due to the nonlinear and nonstationary nature of financial time series. This study proposes a hybrid variational mode decomposition (VMD)–generalized autoregressive conditional heteroskedasticity (GARCH)–long short-term
John Kamwele Mutinda, Amos Kipkorir Langat, Samuel Musili Mwalili   +2 more
doaj   +1 more source

Dynamic Hyperbolic Tangent PSO-Optimized VMD for Pressure Signal Denoising and Prediction in Water Supply Networks

Entropy
Urban water supply networks are prone to complex noise interference, which significantly degrades the performance of data-driven forecasting models. Conventional denoising techniques, such as standard Variational Mode Decomposition (VMD), often rely on ...
Yujie Shang, Zheng Zhang
doaj   +1 more source

Enhancing agricultural sustainability: Time series forecasting with ICEEMDAN-VMD-GRU for economic-resilience

Results in Engineering
In this study, we offer a dual-decomposition hybrid time series forecasting model that combines improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and variational mode decomposition (VMD) with gated recurrent unit (GRU)
Aastha M. Sathe, Supraja R., Aditya Antony Thomas   +2 more
doaj   +1 more source

An Improved Sparrow Search Algorithm for the Optimization of Variational Modal Decomposition Parameters

Applied Sciences
Variational modal decomposition (VMD) is frequently employed for both signal decomposition and extracting features; however, the decomposition outcome is influenced by the quantity of intrinsic modal functions (IMFs) and the specific parameter values of ...
Haoran Du, Jixin Wang, Wenjun Qian, Xunan Zhang   +3 more
doaj   +1 more source

New 2D-Variational Mode Decomposition-Based Techniques for Seismic Attribute Enhancement

Applied Sciences
Seismic attributes are widely used to enhance the interpretation of structural, stratigraphic, and lithologic features in subsurface data. Their effectiveness, however, can be limited by noise, resolution constraints, and processing artifacts. This study
Said Gaci, Mohammed Farfour
doaj   +1 more source

A combined model for short-term traffic flow prediction based on variational modal decomposition and deep learning

Scientific Reports
The emergence of Deep Learning provides an opportunity for traffic flow prediction. However, uncertainty and volatility exhibited by nonlinearity and instability of traffic flow pose challenges to Deep Learning models.
Chuanxiang Ren, Fangfang Fu, Changchang Yin, Li Lu, Lin Cheng   +4 more
doaj   +1 more source

Short-term wind power forecasting based on SSA-VMD-LSTM

Energy Reports, 2023
Xiaozhi Gao, Wang Guo, Chunxiao Mei, Jitong Sha, Yingjun Guo, Hexu Sun   +5 more
semanticscholar   +1 more source

VMD versus PCAC in πo → γγ

Physics Letters B, 1973
Abstract A conflict between strong PCAC and VMD is pointed out in the decay π o → γγ . A solution of the problem is presented in the weak PCAC framework.
openaire   +1 more source

A Fault Diagnosis Method for Rolling Bearings Based on Enhanced Sparrow Search Algorithm-Optimized VMD and CNN-BiLSTM. [PDF]

Sensors (Basel)
Liu F, Yue X.
europepmc   +1 more source

Multi-Feature Adaptive Variational Mode Decomposition for Wearable ECG Devices. [PDF]

Biosensors (Basel)
Chen Z, Wu D, Nie Y, Zhang J, Liu G, He F, Mo L, Peng L, Zeng C, Liu Z.   +9 more
europepmc   +1 more source