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Time series forecasting for nonlinear and non-stationary processes: a review and comparative study
C Cheng, A Sa-Ngasoongsong, O Beyca, T Le… - Iie …, 2015 - Taylor & Francis
Forecasting the evolution of complex systems is noted as one of the 10 grand challenges of
modern science. Time series data from complex systems capture the dynamic behaviors and …
modern science. Time series data from complex systems capture the dynamic behaviors and …
Successive variational mode decomposition
M Nazari, SM Sakhaei - Signal Processing, 2020 - Elsevier
Variational mode decomposition (VMD) is a powerful technique for concurrently
decomposing a signal into its constituent intrinsic modes. However, the performance of VMD …
decomposing a signal into its constituent intrinsic modes. However, the performance of VMD …
New insights and best practices for the successful use of Empirical Mode Decomposition, Iterative Filtering and derived algorithms
A Stallone, A Cicone, M Materassi - Scientific reports, 2020 - nature.com
Abstract Algorithms based on Empirical Mode Decomposition (EMD) and Iterative Filtering
(IF) are largely implemented for representing a signal as superposition of simpler well …
(IF) are largely implemented for representing a signal as superposition of simpler well …
A robust method for non-stationary streamflow prediction based on improved EMD-SVM model
E Meng, S Huang, Q Huang, W Fang, L Wu, L Wang - Journal of hydrology, 2019 - Elsevier
Monthly streamflow prediction can offer important information for optimal management of
water resources, flood mitigation, and drought warning. The semi-humid and semi-arid Wei …
water resources, flood mitigation, and drought warning. The semi-humid and semi-arid Wei …
Uniformly elevated future heat stress in China driven by spatially heterogeneous water vapor changes
F Wang, M Gao, C Liu, R Zhao, MB McElroy - Nature Communications, 2024 - nature.com
The wet bulb temperature (Tw) has gained considerable attention as a crucial indicator of
heat-related health risks. Here we report south-to-north spatially heterogeneous trends of Tw …
heat-related health risks. Here we report south-to-north spatially heterogeneous trends of Tw …
Monthly streamflow prediction using modified EMD-based support vector machine
S Huang, J Chang, Q Huang, Y Chen - Journal of Hydrology, 2014 - Elsevier
It is of great significance for operation, planning and dispatching of hydropower station to
predict monthly streamflow accurately. Therefore, the main goal of this study is to investigate …
predict monthly streamflow accurately. Therefore, the main goal of this study is to investigate …
The influence of anthropogenic aerosol on multi-decadal variations of historical global climate
LJ Wilcox, EJ Highwood… - Environmental Research …, 2013 - iopscience.iop.org
Abstract Analysis of single forcing runs from CMIP5 (the fifth Coupled Model Intercomparison
Project) simulations shows that the mid-twentieth century temperature hiatus, and the …
Project) simulations shows that the mid-twentieth century temperature hiatus, and the …
Numerical analysis for iterative filtering with new efficient implementations based on FFT
The development of methods able to extract hidden features from non-stationary and non-
linear signals in a fast and reliable way is of high importance in many research fields. In this …
linear signals in a fast and reliable way is of high importance in many research fields. In this …
EMD and LSTM hybrid deep learning model for predicting sunspot number time series with a cyclic pattern
T Lee - Solar physics, 2020 - Springer
The prediction of a time series such as climate indices and the sunspot number (SSN) with
long-term oscillatory behaviors has been a challenging task due to the complex combination …
long-term oscillatory behaviors has been a challenging task due to the complex combination …
Streamflow estimation by support vector machine coupled with different methods of time series decomposition in the upper reaches of Yangtze River, China
S Zhu, J Zhou, L Ye, C Meng - Environmental Earth Sciences, 2016 - Springer
Abstract Machine learning models combined with time series decomposition are widely
employed to estimate streamflow, yet the effect of the utilization of different decomposing …
employed to estimate streamflow, yet the effect of the utilization of different decomposing …