Geomagnetic variation on decadal time scales: What can we learn from Empirical Mode Decomposition?

2010 ◽  
Vol 37 (14) ◽  
pp. n/a-n/a ◽  
Author(s):  
L. P. Jackson ◽  
J. E. Mound
Keyword(s):  
Empirical Mode Decomposition ◽  
Time Scales ◽  
Geomagnetic Variation ◽  
Mode Decomposition

Analysis the Reasons for Low Water Level Emerged in Poyang Lake Based on Hydrological Long-Time Series Decomposition by EMD

2013 ◽  
Vol 295-298 ◽  
pp. 1941-1947
Author(s):  
Yu Ru Lin ◽  
Yan Jun Kong ◽  
Tao Yan
Keyword(s):  
Time Series ◽  
Water Level ◽  
Empirical Mode Decomposition ◽  
Time Scales ◽  
Yangtze River ◽  
Poyang Lake ◽  
Short Scale ◽  
Long Time Series ◽  
Mode Decomposition ◽  
Long Time

The wet and dry periods with multi-time scales of hydrological long-time series in Poyang Lake and Yangtze River were analyzed based on the method of Empirical Mode Decomposition (EMD). The results indicated that the variation of wet and dry periods of Yangtze River and Poyang Lake had diversified representation, and consistency with the meso and short scale periods. The reasons for the low water level emerged early and the lowest water level had breakthrough the history were explained.

Cointegration and the Empirical Mode Decomposition for the Analysis of Diagnostic Data

2013 ◽  
Vol 569-570 ◽  
pp. 884-891 ◽  
Author(s):  
Ifigeneia Antoniadou ◽  
Elizabeth J. Cross ◽  
Keith Worden
Keyword(s):  
Empirical Mode Decomposition ◽  
Time Scales ◽  
Energy Operator ◽  
Frequency Separation ◽  
Operational Conditions ◽  
Multi Scale ◽  
Mode Decomposition ◽  
Detection Approach ◽  
Empirical Mode Decomposition Method ◽  
The Hilbert Transform

The use of cointegration has been proposed recently as a potentially powerful means of removing confounding influences from structural health monitoring (SHM) data. On the other hand the Empirical Mode Decomposition method is a recent multi-scale decomposition technique with the ability to decompose a signal into meaningful signal components. In this paper the EMD method will be used to highlight the dominant time-scales that have been affected by varying environmental and operational conditions and the time-scales that are related to damage. Then cointegration will be used to remove the nonstationary effects not associated with damage at the time-scales of interest in the data. The final step of the damage detection approach proposed, will be the use of two different amplitude-frequency separation methods, the Hilbert Transform and the more recent Teager Kaiser energy operator approach in order to compare the merits of both, concerning the estimation of the instantaneous characteristics of the signals.

scholarly journals Prediction of mean monthly river discharges in Colombia through Empirical Mode Decomposition

2015 ◽  
Vol 366 ◽  
pp. 172-172 ◽  
Author(s):  
A. M. Carmona ◽  
G. Poveda
Keyword(s):  
Neural Networks ◽  
Empirical Mode Decomposition ◽  
Time Scales ◽  
Predictive Analytics ◽  
Intrinsic Mode Functions ◽  
Hydropower Generation ◽  
Traditional Methods ◽  
Territorial Planning ◽  
Mode Decomposition ◽  
Hydro Climatology

Abstract. The hydro-climatology of Colombia exhibits strong natural variability at a broad range of time scales including: inter-decadal, decadal, inter-annual, annual, intra-annual, intra-seasonal, and diurnal. Diverse applied sectors rely on quantitative predictions of river discharges for operational purposes including hydropower generation, agriculture, human health, fluvial navigation, territorial planning and management, risk preparedness and mitigation, among others. Various methodologies have been used to predict monthly mean river discharges that are based on "Predictive Analytics", an area of statistical analysis that studies the extraction of information from historical data to infer future trends and patterns. Our study couples the Empirical Mode Decomposition (EMD) with traditional methods, e.g. Autoregressive Model of Order 1 (AR1) and Neural Networks (NN), to predict mean monthly river discharges in Colombia, South America. The EMD allows us to decompose the historical time series of river discharges into a finite number of intrinsic mode functions (IMF) that capture the different oscillatory modes of different frequencies associated with the inherent time scales coexisting simultaneously in the signal (Huang et al. 1998, Huang and Wu 2008, Rao and Hsu, 2008). Our predictive method states that it is easier and simpler to predict each IMF at a time and then add them up together to obtain the predicted river discharge for a certain month, than predicting the full signal. This method is applied to 10 series of monthly mean river discharges in Colombia, using calibration periods of more than 25 years, and validation periods of about 12 years. Predictions are performed for time horizons spanning from 1 to 12 months. Our results show that predictions obtained through the traditional methods improve when the EMD is used as a previous step, since errors decrease by up to 13% when the AR1 model is used, and by up to 18% when using Neural Networks is combined with the EMD.

scholarly journals Traffic Flow Prediction at Varied Time Scales via Ensemble Empirical Mode Decomposition and Artificial Neural Network

2020 ◽  
Vol 12 (9) ◽  
pp. 3678 ◽  
Author(s):  
Xinqiang Chen ◽  
Jinquan Lu ◽  
Jiansen Zhao ◽  
Zhijian Qu ◽  
Yongsheng Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Traffic Flow ◽  
Empirical Mode Decomposition ◽  
Time Scales ◽  
Ensemble Empirical Mode Decomposition ◽  
Flow Data ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Mode Decomposition

Accurate traffic flow data is crucial for traffic control and management in an intelligent transportation system (ITS), and thus traffic flow prediction research attracts significant attention in the transportation community. Previous studies have suggested that raw traffic flow data may be contaminated by noises caused by unexpected reasons (e.g., loop detector damage, roadway maintenance, etc.), which may degrade traffic flow prediction accuracy. To address this issue, we proposed an ensemble framework via ensemble empirical mode decomposition (EEMD) and artificial neural network (ANN) to predict traffic flow under different time intervals ahead. More specifically, the proposed framework firstly employed the EEMD model to suppress the noises in the raw traffic data, which were then processed to predict traffic flow at time steps under different time scales (i.e., 1, 2, and 10 min). We verified our model performance on three loop detectors’ data, which were supported by the Department of Transportation, Minnesota. The research findings can help traffic participants collect more accurate traffic flow data and thus benefits transportation practitioners by helping them to make more reasonable traffic decisions.

Fast Multidimensional Ensemble Empirical Mode Decomposition Using a Data Compression Technique

2014 ◽  
Vol 27 (10) ◽  
pp. 3492-3504 ◽  
Author(s):  
Jiaxin Feng ◽  
Zhaohua Wu ◽  
Guosheng Liu
Keyword(s):  
Climate Variability ◽  
Empirical Mode Decomposition ◽  
Time Scales ◽  
Grid Point ◽  
Ensemble Empirical Mode Decomposition ◽  
Empirical Orthogonal Functions ◽  
Climate Data ◽  
Climate Variability And Change ◽  
Compression Technique ◽  
Mode Decomposition

Abstract The process of obtaining key information on climate variability and change from large climate datasets often involves large computational costs and removal of noise from the data. In this study, the authors accelerate the computation of a newly developed, multidimensional temporal–spatial analysis method, namely multidimensional ensemble empirical mode decomposition (MEEMD), for climate studies. The original MEEMD uses ensemble empirical mode decomposition (EEMD) to decompose the time series at each grid point and then pieces together the temporal–spatial evolution of climate variability and change on naturally separated time scales, which is computationally expensive. To accelerate the algorithm, the original MEEMD is modified by 1) using principal component analysis (PCA) to transform the original temporal–spatial multidimensional climate data into principal components (PCs) and corresponding empirical orthogonal functions (EOFs); 2) retaining only a small fraction of PCs and EOFs that contain spatially and temporally coherent structures; 3) decomposing PCs into oscillatory components on naturally separated time scales; and 4) obtaining the original MEEMD components on naturally separated time scales by summing the contributions of the similar time scales from different pairs of EOFs and PCs. The study analyzes extended reconstructed sea surface temperature (ERSST) to validate the accelerated (fast) MEEMD. It is demonstrated that, for ERSST climate data, the fast MEEMD can 1) compress data with a compression rate of one to two orders and 2) increase the speed of the original MEEMD algorithm by one to two orders.

scholarly journals An Empirical Mode Decomposition Fuzzy Forecast Model for Air Quality

2021 ◽  
Author(s):  
Bo-Lun Chen ◽  
Guo-Chang Zhu ◽  
Yi-Yun Sheng ◽  
Qian Xie ◽  
Min Ji ◽  
...  
Keyword(s):  
Air Quality ◽  
Empirical Mode Decomposition ◽  
Time Scales ◽  
Learning Ability ◽  
Quality Data ◽  
Physical And Mental Health ◽  
Inference System ◽  
Mode Decomposition ◽  
Change Trend ◽  
Different Time Scales

Abstract Air quality is related to people's health. Severe air pollution can cause respiratory diseases, while good air quality is beneficial to physical and mental health. Therefore, the prediction of air quality is very important. As an important algorithm for signal analysis, empirical mode decomposition can analyze the change trend of air quality well, smooth the complex and changeable air quality data, and get the change trend of air quality under different time scales. According to the change trend under different time scales, the extreme learning machine is used for training, and the corresponding prediction value is obtained. The adaptive fuzzy inference system is used for fitting to obtain the final air quality prediction result. The experimental results show that the signal decomposition fuzzy prediction model has a good learning ability and has good accuracy in predicting the concentration of various pollutants in air quality.

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