scholarly journals Dynamic correlations at different time-scales with empirical mode decomposition

2018 ◽  
Vol 502 ◽  
pp. 534-544 ◽  
Author(s):  
Noemi Nava ◽  
T. Di Matteo ◽  
Tomaso Aste
Keyword(s):  
Empirical Mode Decomposition ◽  
Time Scales ◽  
Dynamic Correlations ◽  
Mode Decomposition ◽  
Different Time Scales

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.

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.

Quantitatively assessing the impacts of climate variability and human activities on runoff

2020 ◽  
Author(s):  
Yanhua Qin ◽  
Xun Sun ◽  
Baofu Li
Keyword(s):  
Climate Variability ◽  
Time Scales ◽  
Hybrid Model ◽  
Human Activities ◽  
Back Propagation ◽  
Impact Factors ◽  
Mode Decomposition ◽  
Runoff Change ◽  
Different Time Scales ◽  
Nonlinear Hybrid

<p>To quantitatively evaluate the impacts of climate variability and human activities on runoff at different time scales is a challenging task. In this study, a nonlinear hybrid model integrating extreme-point symmetric mode decomposition, back propagation artificial neural networks and weights connection method based on the physical nonlinear relationship between impact factors and runoff were developed to explore an approach for solving this problem. To validate the applicability of the nonlinear hybrid model, the Hotan River was employed to assess the impacts of climate variability and human activities on runoff. Results illustrated that a good performance was presented by this model. The contribution of the upper-air temperature at 500 hPa was the highest (70.5%), which is the most important factor for runoff change. At different time scales, this factor also has the highest contributions. However, the water vapor content was responsible for 22.0% of the runoff change. Furthermore, the human activities were only accounted for 7.5%, indicating that runoff in the Hotan River is more sensitive to climate variability than human activities.</p>

scholarly journals Complexity Analysis of Precipitation and Runoff Series Based on Approximate Entropy and Extreme-Point Symmetric Mode Decomposition

Water ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1388 ◽  
Author(s):  
Dongyong Sun ◽  
Hongbo Zhang ◽  
Zhihui Guo
Keyword(s):  
Climate Change ◽  
Correlation Coefficient ◽  
Extreme Point ◽  
Time Scales ◽  
Approximate Entropy ◽  
Monthly Precipitation ◽  
Symmetric Mode ◽  
Mode Decomposition ◽  
Different Time Scales ◽  
The Imf

Many regional hydrological regime changes are complex under the influences of climate change and human activities, which make it difficult to understand the regional or basin al hydrological status. To investigate the complexity of precipitation and the runoff time series from 1960 to 2012 in the Jing River Basin on different time scales, approximate entropy, a Bayesian approach and extreme-point symmetric mode decomposition were employed. The results show that the complexity of annual precipitation and runoff has decreased since the 1990sand that the change occurred in 1995. The Intrinsic Mode Function (IMF)-6 component decomposed by extreme-point symmetric mode decomposition of monthly precipitation and runoff was consistent with precipitation and runoff. The IMF-6 component of monthly precipitation closely followed the 10-year cycle of change, and it has an obvious correlation with sunspots. The correlation coefficient is 0.6, representing a positive correlation before 1995 and a negative correlation after 1995. However, the IMF-6 component of monthly runoff does not have a significant correlation with sunspots, and the correlation coefficient is only 0.41, which indicates that climate change is not the dominant factor of runoff change. Approximate entropy is an effective analytical method for complexity, and furthermore, it can be decomposed by extreme-point symmetric mode decomposition to obtain the physical process of the sequences at different time scales, which helps us to understand the background of climate change and human activity in the process of precipitation and runoff.

scholarly journals Multiscale Market Integration and Nonlinear Granger Causality between Natural Gas Futures and Physical Markets

2019 ◽  
Vol 11 (19) ◽  
pp. 5518
Author(s):  
Cuilin Li ◽  
Ya-Juan Du ◽  
Qiang Ji ◽  
Jiang-bo Geng
Keyword(s):  
Natural Gas ◽  
Time Scales ◽  
Market Integration ◽  
Spanning Trees ◽  
Original Data ◽  
Ensemble Empirical Mode Decomposition ◽  
Minimum Spanning Trees ◽  
Mode Decomposition ◽  
Eemd Method ◽  
Different Time Scales

This paper comprehensively analyzed the price integration of the U.S. natural gas futures market and its physical markets. The analyses were conducted in the form of graphics using the ensemble empirical mode decomposition (EEMD) method and minimum spanning trees with various horizons. Our findings indicated that the network structures of the minimum spanning trees of the gas futures and physical markets are the same on different time scales. The citygate returns were always the core of the physical gas markets. In addition, the gas futures and physical markets were highly integrated on different time scales. Moreover, our findings showed that at the original data level, unidirectional linear and nonlinear causalities from gas futures to physical returns exist. Specifically, the relationships between futures and physical gas returns were not constant across various time scales. In the long term, futures gas returns had only a linear causality with the citygate, commercial, and industry gas returns, and a unidirectional, nonlinear causality with residential gas returns.

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.

scholarly journals Possible link between Holocene East Asian monsoon and solar activity obtained from the EMD method

2012 ◽  
Vol 19 (4) ◽  
pp. 421-430 ◽  
Author(s):  
H. Y. Liu ◽  
Z. S. Lin ◽  
X. Z. Qi ◽  
Y. X. Li ◽  
M. T. Yu ◽  
...  
Keyword(s):  
Solar Activity ◽  
Time Scales ◽  
Asian Monsoon ◽  
East Asian Monsoon ◽  
East Asian ◽  
Activity Time ◽  
Mode Decomposition ◽  
Common Cycles ◽  
Emd Method ◽  
Different Time Scales

Abstract. It is thought that East Asian monsoon (EAM) is linked and sensitive to solar activity. In this paper, we have decomposed the Dongge cave speleothem δ18O record (proxy for EAM), and Δ14C and 10Be (proxies for solar activity) time series into variations at different time scales with the empirical mode decomposition (EMD) method to reveal the possible link between the EAM variability and solar activity. There are some common cycles in the EAM and solar variability from centennial to millennial scales, indicating a possible link between EAM and solar activity at these time scales. The correlation between EAM and solar activity is much higher at millennial scales than at centennial scales, which means direct responses to the solar variation are more likely at time scales longer than a few hundred years. At ~30, 60 and 600 yr time scales, the variation in EAM is amplified by the solar amplitude modulation at ~100, 200 and 2200 yr time scales.

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