An improved framework to predict river flow time series data

An improved framework to predict river flow time series data

Due to non-stationary and noise characteristics of river flow time series data, some pre-processing methods are adopted to address the multi-scale and noise complexity. In this paper, we proposed an improved framework comprising Complete Ensemble Empirical Mode Decomposition with Adaptive Noise-Empi...

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Bibliographic Details
Main Authors: Hafiza Mamona Nazir, Ijaz Hussain, Ishfaq Ahmad, Muhammad Faisal, Ibrahim M. Almanjahie
Format: Article
Language:English
Published: PeerJ Inc. 2019-07-01
Series:PeerJ
Subjects:
Empirical Mode Decomposition (EMD)
Wavelet Analysis (WA)
Machine Learning (ML)
Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Empirical Bayes Threshold (EBT)
Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Empirical Bayes Threshold (EBT).
Online Access:https://peerj.com/articles/7183.pdf

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https://peerj.com/articles/7183.pdf

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