An improved genetic algorithm for optimizing ensemble empirical mode decomposition method

Zhang, Dabin; Cai, Chaomin; Chen, Shanying; Ling, Liwen

doi:10.1080/21642583.2019.1627598

Cited by 11 publications

(8 citation statements)

References 19 publications

(28 reference statements)

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“…In most of the cases, these hyperparameters are defined by trial and error procedure [37,60,62]. However, this paper proposes the use of the COA approach to minimize the inverse of the orthogonal index (OI) [16]. The OI is used to measure the orthogonality of the EMD numerically, and a value close to zero is desirable.…”

Section: Complementary Ensemble Empirical Mode Decompositionmentioning

confidence: 99%

“…Through this process, the diversity is enhanced and, when a final efficient forecasting model is obtained by aggregation (directly aggregation), an efficient model is obtained. Alongside this, evolutionary computation and swarm intelligence algorithms can be used to tune the hyperparameters of machine learning models [15] or the time series decomposition methods [16], aiming to make the model more accurate.…”

Section: Introductionmentioning

confidence: 99%

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Electricity Price Forecasting Based on Self-Adaptive Decomposition and Heterogeneous Ensemble Learning

et al. 2020

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Electricity price forecasting plays a vital role in the financial markets. This paper proposes a self-adaptive, decomposed, heterogeneous, and ensemble learning model for short-term electricity price forecasting one, two, and three-months-ahead in the Brazilian market. Exogenous variables, such as supply, lagged prices and demand are considered as inputs signals of the forecasting model. Firstly, the coyote optimization algorithm is adopted to tune the hyperparameters of complementary ensemble empirical mode decomposition in the pre-processing phase. Next, three machine learning models, including extreme learning machine, gradient boosting machine, and support vector regression models, as well as Gaussian process, are designed with the intent of handling the components obtained through the signal decomposition approach with focus on time series forecasting. The individual forecasting models are directly integrated in order to obtain the final forecasting prices one to three-months-ahead. In this case, a grid of forecasting models is obtained. The best forecasting model is the one that has better generalization out-of-sample. The empirical results show the efficiency of the proposed model. Additionally, it can achieve forecasting errors lower than 4.2% in terms of symmetric mean absolute percentage error. The ranking of importance of the variables, from the smallest to the largest is, lagged prices, demand, and supply. This paper provided useful insights for multi-step-ahead forecasting in the electrical market, once the proposed model can enhance forecasting accuracy and stability.

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Section: Complementary Ensemble Empirical Mode Decompositionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Electricity Price Forecasting Based on Self-Adaptive Decomposition and Heterogeneous Ensemble Learning

et al. 2020

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“…Furthermore, EMD-related methods are prone to mode mixing [ 14 , 15 , 16 ], end effects [ 14 , 15 ] and detrend uncertainty [ 15 ]. Therefore, a new noise-assisted data analysis tool, called ensemble empirical mode decomposition (EEMD) [ 14 ], was proposed to reduce mode mixing and end effects.…”

Section: Introductionmentioning

confidence: 99%

“…Zhang et al [ 16 ], with the help of an improved genetic algorithm (GA), also proposed an improved ensemble empirical mode decomposition method (GAEEMD) to solve mode mixing. In the improved GA, Zhang et al used a difference selection operator instead of a traditional selection operator (roulette selection or tournament selection) and selected the amplitudes of the added white noise and the number of trials as the parameters of their fitness function, which was the reciprocal of an orthogonal index concerning the decomposed IMFs.…”

Section: Introductionmentioning

confidence: 99%

Using an Optimization Algorithm to Detect Hidden Waveforms of Signals

Chang

2021

Sensors

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Source signals often contain various hidden waveforms, which further provide precious information. Therefore, detecting and capturing these waveforms is very important. For signal decomposition (SD), discrete Fourier transform (DFT) and empirical mode decomposition (EMD) are two main tools. They both can easily decompose any source signal into different components. DFT is based on Cosine functions; EMD is based on a collection of intrinsic mode functions (IMFs). With the help of Cosine functions and IMFs respectively, DFT and EMD can extract additional information from sensed signals. However, due to a considerably finite frequency resolution, EMD easily causes frequency mixing. Although DFT has a larger frequency resolution than EMD, its resolution is also finite. To effectively detect and capture hidden waveforms, we use an optimization algorithm, differential evolution (DE), to decompose. The technique is called SD by DE (SDDE). In contrast, SDDE has an infinite frequency resolution, and hence it has the opportunity to exactly decompose. Our proposed SDDE approach is the first tool of directly applying an optimization algorithm to signal decomposition in which the main components of source signals can be determined. For source signals from four combinations of three periodic waves, our experimental results in the absence of noise show that the proposed SDDE approach can exactly or almost exactly determine their corresponding separate components. Even in the presence of white noise, our proposed SDDE approach is still able to determine the main components. However, DFT usually generates spurious main components; EMD cannot decompose well and is easily affected by white noise. According to the superior experimental performance, our proposed SDDE approach can be widely used in the future to explore various signals for more valuable information.

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“…The other type of de-noising is processing the noise in the frequency domain. The discrete wavelet transform (DWT) method [27][28][29][30][31][32][33][34][35][36], ensemble empirical mode decomposition (EEMD) method [37][38][39][40], and fast Fourier transform (FFT) method [41][42][43] have been hot topics in processing traffic flow measurement noises in recent years. The DWT method combined with Daubechies 4 wavelet has been used to deal with traffic flow data, and an improvement in forecasting accuracy has been achieved [22].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Cutoff Frequency Selection Approach for Fast Fourier Transform Method and Its Application into Short-Term Traffic Flow Forecasting

Wang

Shi

Liu

et al. 2020

IJGI

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Historical measurements are usually used to build assimilation models in sequential data assimilation (S-DA) systems. However, they are always disturbed by local noises. Simultaneously, the accuracy of assimilation model construction and assimilation forecasting results will be affected. The fast Fourier transform (FFT) method can be used to acquire de-noised historical traffic flow measurements to reduce the influence of local noises on constructed assimilation models and improve the accuracy of assimilation results. In the practical signal de-noising applications, the FFT method is commonly used to de-noise the noisy signal with known noise frequency. However, knowing the noise frequency is difficult. Thus, a proper cutoff frequency should be chosen to separate high-frequency information caused by noises from the low-frequency part of useful signals under the unknown noise frequency. If the cutoff frequency is too high, too much noisy information will be treated as useful information. Conversely, if the cutoff frequency is too low, part of the useful information will be lost. To solve this problem, this paper proposes an adaptive cutoff frequency selection (A-CFS) method based on cross-validation. The proposed method can determine a proper cutoff frequency and ensure the quality of de-noised outputs for a given dataset using the FFT method without noise frequency information. Experimental results of real-world traffic flow data measurements in a sub-area of a highway near Birmingham, England, demonstrate the superior performance of the proposed A-CFS method in noisy information separation using the FFT method. The differences between true and predicted traffic flow values are evaluated using the mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage (MAPE) values. Compared to the results of the two commonly used de-noising methods, i.e., discrete wavelet transform (DWT) and ensemble empirical mode decomposition (EEMD) methods, the short-term traffic flow forecasting results of the proposed A-CFS method are much more reliable. In terms of the MAE value, the average relative improvements of the assimilation model built using the proposed method are 19.26%, 3.47%, and 4.25%, compared to the model built using raw data, DWT method, and EEMD method, respectively; the corresponding average relative improvements in RMSE are 19.05%, 5.36%, and 3.02%, respectively; lastly, the corresponding average relative improvements in MAPE are 18.88%, 2.83%, and 2.28%, respectively. The test results show that the proposed method is effective in separating noises from historical measurements and can improve the accuracy of assimilation model construction and assimilation forecasting results.

show abstract

An improved genetic algorithm for optimizing ensemble empirical mode decomposition method

Cited by 11 publications

References 19 publications

Electricity Price Forecasting Based on Self-Adaptive Decomposition and Heterogeneous Ensemble Learning

Electricity Price Forecasting Based on Self-Adaptive Decomposition and Heterogeneous Ensemble Learning

Using an Optimization Algorithm to Detect Hidden Waveforms of Signals

An Adaptive Cutoff Frequency Selection Approach for Fast Fourier Transform Method and Its Application into Short-Term Traffic Flow Forecasting

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