Empirical Mode Decomposition–Autoregressive Integrated Moving Average

2014 ◽  
Vol 2460 (1) ◽  
pp. 66-76 ◽  
Author(s):  
Haizhong Wang ◽  
Lu Liu ◽  
Zhen (Sean) Qian ◽  
Heng Wei ◽  
Shangjia Dong
Keyword(s):  
Empirical Mode Decomposition ◽  
Moving Average ◽  
Autoregressive Integrated Moving Average ◽  
Mode Decomposition

Time Series Preprocessing and Forecasting Based on EMD

2013 ◽  
Vol 313-314 ◽  
pp. 1256-1261
Author(s):  
Guo Chen Feng ◽  
Peng Jian Shang ◽  
Xue Jiao Wang
Keyword(s):  
Time Series ◽  
Empirical Mode Decomposition ◽  
Moving Average ◽  
Original Data ◽  
Autoregressive Integrated Moving Average ◽  
Mode Decomposition ◽  
Moving Average Processes

In this paper we pay attention to the preprocessing of time series and its application. We apply Empirical Mode Decomposition (EMD) to decompose three kinds of series into their components in order to study the data and forecast more efficiently. We try to unite EMD analysis and autoregressive integrated moving average processes (ARIMA) into a new forecasting technique which we call EMD-ARIMA. We find that our method is extraordinarily close to the original data.

scholarly journals Empirical Mode Decomposition of the Acceleration Response of a Prismatic Beam Subject to a Moving Load to Identify Multiple Damage Locations

2012 ◽  
Vol 19 (5) ◽  
pp. 845-856 ◽  
Author(s):  
J. Meredith ◽  
A. González ◽  
D. Hester
Keyword(s):  
Finite Element ◽  
Empirical Mode Decomposition ◽  
Moving Average ◽  
Moving Load ◽  
Element Model ◽  
Detection Algorithm ◽  
Measured Signal ◽  
Intrinsic Mode Functions ◽  
Acceleration Response ◽  
Mode Decomposition

Empirical Mode Decomposition (EMD) is a technique that converts the measured signal into a number of basic functions known as intrinsic mode functions. The EMD-based damage detection algorithm relies on the principle that a sudden loss of stiffness in a structural member will cause a discontinuity in the measured response that can be detected through a distinctive spike in the filtered intrinsic mode function. Recent studies have shown that applying EMD to the acceleration response, due to the crossing of a constant load over a beam finite element model, can be used to detect a single damaged location. In this paper, the technique is further tested using the response of a discretized finite element beam with multiple damaged sections modeled as localized losses of stiffness. The ability of the algorithm to detect more than one damaged section is analysed for a variety of scenarios including a range of bridge lengths, speeds of the moving load and noise levels. The use of a moving average filter on the acceleration response, prior to applying EMD, is shown to improve the sensitivity to damage. The influence of the number of measurement points and their distance to the damaged sections on the accuracy of the predicted damage is also discussed.

scholarly journals PENDUGAAN ALIRAN SUNGAI BAWAH TANAH DI DESA HARGOSARI GUNUNGKIDUL YOGYAKARTA MENGGUNAKAN METODE VERY LOW FREQUENCY-ELECTROMAGNETIC (VLF-EM) DENGAN FILTER NOISE ASSISTED- MULTIVARIATE EMPIRICAL MODE DECOMPOSITION (NA-MEMD)

2018 ◽  
Vol 8 (1) ◽  
pp. 74
Author(s):  
Yesi Yusmita
Keyword(s):  
Empirical Mode Decomposition ◽  
Moving Average ◽  
Low Frequency ◽  
Very Low Frequency ◽  
Multivariate Empirical Mode Decomposition ◽  
Mode Decomposition ◽  
Filter Noise

AbstrakAir di kawasan karst mengalir melalui sistem retakan celah gua. Air bawah permukaan akan terakumulasi dan mengalir dalam suatu pola aliran tertentu melalui lorong-lorong gua yang pada akhirnya membentuk sungai bawah tanah. Sungai bawah tanah daerah karst terkadang dapat berada sejajar horizontal namun alirannya berlawanan, bahkan dapat juga saling menyilang bertingkat tidak saling berhubungan. Salah satu daerah yang memiliki kondisi seperti itu adalah desa Hargosari, kabupaten Gunungkidul, Yogyakarta. Sebenarnya di daerah tersebut banyak terdapat air sungai bawah tanah sekalipun di musim kemarau, namun posisi serta kedalamannya belum diketahui, sehingga dilakukan penelitian dengan metode geofisika yaitu metode Elektromagnetik Very Low Frequency untuk mengestimasi keberadaan dan kedalaman sungai bawah tanah tersebut. Data lapangan yang didapatkan dari hasil pengukuran metode VLF-EM biasanya tercampur dengan noise dan outlier, untuk itu digunakan filter NA-MEMD (Noise Assisted-Multivariate Empirical Mode Decomposition) yang mampu mereduksi noise dan outlier dari data pengukuran, dan juga  menggunakan  filter Moving Average, dan filter Karous H-jelt untuk menghasilkan kontur rapat arus ekivalen sehingga posisi sungai bawah tanah dapat diketahui.Hasil interpretasi rapat arus ekivalen menunjukan arah sungai bawah tanah di desa Hargosari ke arah barat mengikuti aliran sebelumnya, dengan kedalaman sungai bawah tanah terlihat pada lintasan 1 berada pada kedalaman 125 m, pada lintasan 2 sungai berada pada kedalaman 120 m, lintasan 3 sungai berada pada kedalaman 112 m, lintasan 4 berada pada kedalaman 105 m, dan untuk lintasan terakhir yaitu lintasan 5 berada pada kedalaman 95 m. Untuk sungai 2, baru terlihat pada lintasan 2 pada kedalaman 115 m, lintasan ke tiga pada kedalaman 110 m, lintasan ke empat pada ke dalaman 100 m, dan untuk lintasan kelima pada kedalaman 80 m.  Berdasarkan hasil penelitian ini, arah aliran sungai bawah tanah di desa Hargosari mengarah ke Barat.Kata Kunci : Sungai bawah tanah, VLF, filter NA-MEMD, filter Karous HJelt, rapat arus ekivalen. 

scholarly journals A Hybrid Forecasting Method for Solar Output Power Based on Variational Mode Decomposition, Deep Belief Networks and Auto-Regressive Moving Average

2018 ◽  
Vol 8 (10) ◽  
pp. 1901 ◽  
Author(s):  
Tuo Xie ◽  
Gang Zhang ◽  
Hongchi Liu ◽  
Fuchao Liu ◽  
Peidong Du
Keyword(s):  
Power Generation ◽  
Time Series ◽  
Empirical Mode Decomposition ◽  
Prediction Models ◽  
Moving Average ◽  
Variational Mode Decomposition ◽  
Mode Decomposition ◽  
Forecasting Method ◽  
Auto Regressive ◽  
Pv Power Generation

Due to the existing large-scale grid-connected photovoltaic (PV) power generation installations, accurate PV power forecasting is critical to the safe and economical operation of electric power systems. In this study, a hybrid short-term forecasting method based on the Variational Mode Decomposition (VMD) technique, the Deep Belief Network (DBN) and the Auto-Regressive Moving Average Model (ARMA) is proposed to deal with the problem of forecasting accuracy. The DBN model combines a forward unsupervised greedy layer-by-layer training algorithm with a reverse Back-Projection (BP) fine-tuning algorithm, making full use of feature extraction advantages of the deep architecture and showing good performance in generalized predictive analysis. To better analyze the time series of historical data, VMD decomposes time series data into an ensemble of components with different frequencies; this improves the shortcomings of decomposition from Empirical Mode Decomposition (EMD) and Ensemble Empirical Mode Decomposition (EEMD) processes. Classification is achieved via the spectrum characteristics of modal components, the high-frequency Intrinsic Mode Functions (IMFs) components are predicted using the DBN, and the low-frequency IMFs components are predicted using the ARMA. Eventually, the forecasting result is generated by reconstructing the predicted component values. To demonstrate the effectiveness of the proposed method, it is tested based on the practical information of PV power generation data from a real case study in Yunnan. The proposed approach is compared, respectively, with the single prediction models and the decomposition-combined prediction models. The evaluation of the forecasting performance is carried out with the normalized absolute average error, normalized root-mean-square error and Hill inequality coefficient; the results are subsequently compared with real-world scenarios. The proposed approach outperforms the single prediction models and the combined forecasting methods, demonstrating its favorable accuracy and reliability.

scholarly journals Peak Traffic Flow Predictions: Exploiting Toll Data from Large Expressway Networks

2020 ◽  
Vol 13 (1) ◽  
pp. 260
Author(s):  
Ling Shen ◽  
Jian Lu ◽  
Dongdong Geng ◽  
Ling Deng
Keyword(s):  
Big Data ◽  
Traffic Flow ◽  
Moving Average ◽  
Arima Model ◽  
Training Data ◽  
Short Term ◽  
Autoregressive Integrated Moving Average ◽  
Mode Decomposition ◽  
The Common ◽  
Model Training

Big data from toll stations provides reliable and accurate origin-destination (OD) pair information of expressway networks. However, although the short-term traffic prediction model based on big data is being constantly improved, the volatility and nonlinearity of peak traffic flow restricts the accuracy of the prediction results. Therefore, this research attempts to solve this problem through three contributions, firstly, proposing the use the Pauta criterion from statistics as the standard for defining the anomaly criteria of expressway traffic flows. Through comparison with the common local outlier factor (LOF) method, the rationality and advantages of the Pauta criterion were expounded. Secondly, adding week attributes to data, and splitting the data based on the similarity characteristics of traffic flow time series in order to improve the accuracy and efficiency of data input. Thirdly, by introducing empirical mode decomposition (EMD) to decompose the signal before autoregressive integrated moving average (ARIMA) model training is carried out. The first two contributions are for efficiency, the third is to deal with the volatility and nonlinearity of the abnormal peak training data. Finally, the model is analyzed, based on the expressway toll data of the Jiangsu Province. The results show that the EMD-ARIMA model has more advantages than the ARIMA model when dealing with fluctuating data.

Damage localization under ambient excitations and non-stationary vibration signals by a new hybrid algorithm for feature extraction and multivariate distance correlation methods

2018 ◽  
Vol 18 (2) ◽  
pp. 347-375 ◽  
Author(s):  
Alireza Entezami ◽  
Hashem Shariatmadar
Keyword(s):  
Feature Extraction ◽  
Empirical Mode Decomposition ◽  
Moving Average ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Autoregressive Moving Average Model ◽  
Mode Decomposition ◽  
Stationary Signals ◽  
Moving Average Model ◽  
Adaptive Noise

Ambient excitations applied to structures may lead to non-stationary vibration responses. In such circumstances, it may be difficult or improper to extract meaningful and significant damage features through methods that mainly rely on the stationarity of data. This article proposes a new hybrid algorithm for feature extraction as a combination of a new adaptive signal decomposition method called improved complete ensemble empirical mode decomposition with adaptive noise and autoregressive moving average model. The major contribution of this algorithm is to address the important issue of feature extraction under ambient vibration and non-stationary signals. The improved complete ensemble empirical mode decomposition with adaptive noise method is an improvement on the well-known ensemble empirical mode decomposition technique by removing redundant intrinsic mode functions. In addition, a novel automatic approach is presented to select the most relevant intrinsic mode functions to damage based on the intrinsic mode function energy level. Fitting an autoregressive moving average model to each selected intrinsic mode function, the model residuals are extracted as the damage-sensitive features. The main limitation is that such features are high-dimensional multivariate time series data, which may make a difficult and time-consuming decision-making process for damage localization. Multivariate distance correlation methods are introduced to cope with this drawback and locate structural damage using the multivariate residual sets of the normal and damaged conditions. The accuracy and robustness of the proposed methods are validated by a numerical shear-building model and an experimental benchmark structure. The effects of sampling frequency and time duration are evaluated as well. Results demonstrate the effectiveness and capability of the proposed methods to extract sufficient and reliable features, identify damage location, and quantify damage severity under ambient excitations and non-stationary signals.

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