Complete Ensemble Empirical Mode Decomposition With Adaptive Noise Research Articles

Short-term load forecasting based on CEEMDAN and Transformer

• Mode decomposition, sample entropy and TR model are combined. • The TR-based hybrid models are used for load prediction ranging from 4h-24h. • The impact of different mode decomposition algorithms is verified. • Analysis is provided to validate the proposed model. Short-term load forecasting (STLF) is an essential part of energy plan, and it is very meaningful for energy management. Recently, some deep learning models have been popular in load forecasting. This study focuses on the Transformer (TR) model which can solve the long memory loss problem by introducing the attention mechanism. A hybrid model incorporating Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), sample entropy (SE) and TR is proposed which is named CEEMDAN-SE-TR. To demonstrate the superiority of the proposed CEEMDAN-SE-TR model, a variety of machine learning models are used as comparison models, the one with the best performance is used to compare with the TR model and the proposed CEEMDAN-SE-TR model. Meanwhile, the Empirical Mode Decomposition (EMD) technique is used to compare with CEEMDAN and validate the superiority of CEEMDAN-SE-TR. In this study, STLF (4h, 8h, 12h, and 24h, respectively) is performed for New York city. The results show that the CEEMDAN-SE-TR gets the best forecasting results of all the comparison models, taking the forecasting result of 24h as an example, mean absolute percentage error (MAPE) is 4.80%, root mean square error (RMSE) is 1.26, R2 is 0.80, and mean absolute error (MAE) is 0.95.

Application of CEEMDAN combined wavelet threshold denoising algorithm to suppressing scattering cluster in underwater lidar

<sec>The echo of underwater lidar often contains a significant quantity of scattering clutters. In order to effectively suppress this scattering clutter and improve the ranging accuracy of underwater lidar, a novel denoising method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and wavelet threshold denoising is proposed.</sec><sec>The CEEMDAN-wavelet threshold denoising algorithm uses the correlation coefficient to select intrinsic mode function (IMF) components obtained from the CEEMDAN decomposition. The IMFs, which are more closely related to the original signal, are selected. Then, the wavelet thresholding denoising algorithm is applied to each of the selected IMFs to perform additional denoising. For each IMF component, specific threshold values are calculated based on their frequency and amplitude characteristics. Subsequently, the wavelet coefficients of the IMF components are processed by using these threshold values. Finally, the denoised IMF components are combined and reconstructed to obtain the final denoised signal. Applying the wavelet threshold denoising algorithm to IMF components can effectively remove noise components that cannot be removed by traditional CEEMDAN partial reconstruction methods. By using the threshold value calculated based on the characteristics of each IMF component, the wavelet thresholding denoising process is improved in comparison with directly using a single threshold value. This approach enhances the algorithm’s adaptability and enables more effective removal of noise from the signal.</sec><sec>We apply the proposed method to underwater ranging experiments. A 532 nm intensity-modulated continuous wave laser is used as a light source. Ranging is performed for a target in water with varying attenuation coefficients. A white polyvinyl chloride (PVC) reflector is used as a target. When the correlation extreme value is directly used to determine the delay at a distance of 3.75 attenuation length, it results in a ranging error of 19.2 cm. However, after applying the proposed method, the ranging error is reduced to 6.2 cm, thus effectively improving the ranging accuracy. These results demonstrate that the method has a significant denoising effect in underwater lidar system.</sec>

Fault transient feature extraction method of 66kV transmission line based on improved wavelet threshold and CEEMDAN-FME

In the 66kV transmission line, the compensation effect of the arc suppression coil, so the amplitude of fault zero sequence current is weak after the occurrence of single-phase grounding responsibility. The fault characteristics are not obvious, and there is environmental noise interference during fault signal acquisition, which further increases the difficulty of ground fault feature extraction. To solve the above problems, a fault transient feature extraction method of 66kV transmission line is proposed, based on improved wavelet threshold and Complete Ensemble Empirical Mode Decomposition with adaptive noise (CEEMDAN) - Fuzzy measure entropy (FME). The characteristics of single-phase grounding transient fault in small current grounding system are analyzed. The transient fault signal was preprocessed by an improved wavelet threshold method, and the transient fault characteristics were extracted accurately by CEEMDAN-FME processing. The proposed method has good anti-noise performance through the simulation comparison and verification of denoising processing and transient feature extraction by MATLAB. It can effectively extract fault features when the interference degree is significant.

Research on Short-Term Traffic Flow Combination Prediction Based on CEEMDAN and Machine Learning

Traffic flow has the characteristics of randomness, complexity, and nonlinearity, which brings great difficulty to the prediction of short-term traffic flow. Based on considering the advantages and disadvantages of various prediction models, this paper proposes a short-term traffic flow prediction model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and machine learning. Firstly, CEEMDAN is used to decompose the original traffic flow time series to obtain multiple component sequences with huge complexity differences. In order to measure the complexity of each component sequence, the permutation entropy of each component sequence is calculated. According to the permutation entropy, the component sequence is divided into three types: high-frequency components, intermediate-frequency components, and low-frequency components. Secondly, according to the different volatility of the three types of components, the high-frequency components, intermediate-frequency components, and low-frequency components are predicted by long short-term memory (LSTM), support vector machine (SVM), and k-nearest neighbor (KNN), respectively. Finally, the accurate traffic flow prediction value can be obtained by the linear superposition of the prediction results of the three component prediction models. Through a measured traffic flow data, the combined model proposed in this paper is compared to the binary gray wolf algorithm–long short-term memory (BGWO-LSTM) model, the improved gray wolf algorithm–support vector machine (IGWO-SVM) model, and the KNN model. The mean square error (MSE) of the combined model is less than that of the BGWO-LSTM model, the IGWO-SVM model, and the KNN model by 41.26, 44.98, and 57.69, respectively. The mean absolute error (MAE) of the combined model is less than that of the BGWO-LSTM model, the IGWO-SVM model, and the KNN model by 2.33, 2.44, and 2.70, respectively. The root mean square error (RMSE) of the combined model is less than that of the BGWO-LSTM model, the IGWO-SVM model, and the KNN model by 2.89, 3.11, and 3.80, respectively. The three error indexes of the combined model are far smaller than those of the other single models. At the same time, the decision coefficient (R2) of the combined model is also closer to 1 compared to the other models, indicating that the prediction result of the combined model is the closest to the actual traffic flow.

A Novel Groundwater Burial Depth Prediction Model Based on Two-Stage Modal Decomposition and Deep Learning.

The variability of groundwater burial depths is critical to regional water management. In order to reduce the impact of high-frequency eigenmodal functions (IMF) generated by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) on the prediction results, variational modal decomposition (VMD) is performed on the high frequency IMF components after the primary modal decomposition. A convolutional neural network-gated recurrent unit prediction model (CNN-GRU) is proposed to address the shortcomings of traditional machine learning which cannot handle correlation information and temporal correlation between time series. The CNN-GRU model can extract the implicit features of the coupling relationship between groundwater burial depth and time series and further predict the groundwater burial depth time series. By comparing the prediction results with GRU, CEEMDAN-GRU, and CEEMDAN-CNN-GRU models, we found that the CEEMDAN-VMD-CNN-GRU prediction model outperformed the other prediction models, with a prediction accuracy of 94.29%, good prediction results, and high model confidence.

Multi-step-ahead significant wave height prediction using a hybrid model based on an innovative two-layer decomposition framework and LSTM

As waves are being developed as a renewable energy source, the development of new predictive algorithms to forecast wave height has garnered considerable interest. This study proposes an innovative hybrid model to predict the wave height, including a two-layer decomposition framework and long short-term memory (LSTM). First, the original wave height series is effectively and quickly decomposed into multiple sub-sequences using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). The refined composite multiscale entropy (RCMSE) is then applied to reconstruct these sub-sequences into high-frequency, medium-frequency, low-frequency, and trend components. The respective LSTM is adopted to predict the medium-frequency, low-frequency, and trend components and obtain the sub-results. Subsequently, a series of modes is obtained by the second decomposition of the high-frequency component with variational mode decomposition (VMD), and the sub-result is obtained by forecasting the modes with ensemble LSTM. Finally, we employ the ensemble LSTM to predict all sub-results and obtain the final wave height prediction result, rather than simply adding up all the sub-results linearly. The proposed hybrid model is tested geographically at two buoy stations in eastern New York and Gulf of Mexico. The results show that the proposed hybrid model is more accurate than other benchmark models.

Fault Diagnosis of Wind Turbine Bearings Based on CEEMDAN-GWO-KELM

To solve the problem of fault signals of wind turbine bearings being weak, not easy to extract, and difficult to identify, this paper proposes a fault diagnosis method for fan bearings based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Grey Wolf Algorithm Optimization Kernel Extreme Learning Machine (GWO-KELM). First, eliminating the interference of noise on the collected vibration signal should be conducted, in which the wavelet threshold denoising approach is used in order to reduce the noise interference with the vibration signal. Next, CEEMDAN is used to decompose the signal after a denoising operation to obtain the multi-group intrinsic mode function (IMF), and the feature vector is selected by combining the correlation coefficients to eliminate the spurious feature components. Finally, the fuzzy entropy for the chosen IMF component is input into the GWO-KELM model as a feature vector for defect detection. After diagnosing the Case Western Reserve University (CWRU) dataset by the method presented in this research, it is found that the method can identify 99.42% of the various bearing states. When compared to existing combination approaches, the proposed method is shown to be more efficient for diagnosing wind turbine bearing faults.

Prediction of gas consumption based on LSTM-BPNN hybrid model

ABSTRACT The economic benefits of iron and steel enterprises require the support of Byproduct gas prediction. A novel hybrid model for data pre-processing is proposed to ensure the stability and accuracy of gas prediction. First, each component obtained from Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) decomposition is processed using Sample Entropy (SE), and the members are denoised and restructured by singular spectrum analysis (SSA). Then, the restructured data are predicted by the Back Propagation Neural Network (BPNN) and Long Short-Term Memory (LSTM) and weighted by Particle Swarm Optimization (PSO). Finally, the data are integrated to obtain the final results. The novel hybrid model makes allowances for the linear and nonlinear characteristics of the series and successfully overcomes the limitations of individual models to get accurate and stable prediction results. The results show that the prediction accuracy is improved by 20% on average by adopting the novel hybrid model.

Changes in Dynamic Stability of Cardiovascular Time Series by Local Largest Lyapunov Exponent in the Time-Frequency Domain in Vasovagal Syncope

Orthostatic stress via a head-up tilt test (HUTT) has been clinically used to rule out vasovagal syncope (VVS), which is generated by an inappropriate autonomic nervous system (ANS) regulation. VVS has been evaluated mainly by hemodynamic variables and linear analysis of heart rate variability. In this study, the estimation of the local largest Lyapunov exponent (LLE[Formula: see text] was used to assess the predictability of the beat-to-beat interval (BBI), systolic (SBP), and diastolic blood pressure (DBP) times series to analyze in a nonlinear and time-varying fashion the impact of HUTT on ANS regulation in young females, healthy and patients with VVS. The estimation of LLEL of the time series was achieved in both the original domain (full-band) and narrow-band signals by the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). Results pointed out that during the orthostatic phase (OPH), the VVS patients showed: (1) significantly higher predictability (less complexity) of the BBI time series, i.e., significant altered cardiac vagal modulation, (2) less predictability of SBP variability suggesting altered sympathetic modulation to the vasculature, and (3) LLEL in conjunction with CEEMDAN evidenced an impairment of cardiovascular autonomic control.

A Short-Term Power Load Forecasting Method of Based on the CEEMDAN-MVO-GRU

Given that the power load data are stochastic and it is difficult to obtain accurate forecasting results by a single algorithm. In this study, a combined forecasting method for short-term power load was proposed based on the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Multiverse optimization algorithm (MVO), and the Gated Recurrent Unit (GRU) based on Rectified Adam (RAdam) optimizer. Firstly, the model uses the CEEMDAN algorithm to decompose the original electric load data into subsequences of different frequencies, and the dominant factors are extracted from the subsequences. Then, a GRU network based on the RAdam optimizer was built to perform the forecasting of the subsequences using the existing subsequences data and the associated influencing factors as the data set. Meanwhile, the parameters of the GRU network were optimized with the MVO optimization algorithm for the prediction problems of different subsequences. Finally, the prediction results of each subsequence were superimposed to obtain the final prediction results. The proposed combined prediction method was implemented in a case study of a substation in Weinan, China, and the prediction accuracy was compared with the traditional prediction method. The prediction accuracy index shows that the Root Mean Square Error of the prediction results of the proposed model is 80.18% lower than that of the traditional method, and the prediction accuracy error is controlled within 2%, indicating that the proposed model is better than the traditional method. This will have a favorable impact on the safe and stable operation of the power grid.

CEEMDAN-IPSO-LSTM: A Novel Model for Short-Term Passenger Flow Prediction in Urban Rail Transit Systems.

Urban rail transit (URT) is a key mode of public transport, which serves for greatest user demand. Short-term passenger flow prediction aims to improve management validity and avoid extravagance of public transport resources. In order to anticipate passenger flow for URT, managing nonlinearity, correlation, and periodicity of data series in a single model is difficult. This paper offers a short-term passenger flow prediction combination model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and long-short term memory neural network (LSTM) in order to more accurately anticipate the short-period passenger flow of URT. In the meantime, the hyperparameters of LSTM were calculated using the improved particle swarm optimization (IPSO). First, CEEMDAN-IPSO-LSTM model performed the CEEMDAN decomposition of passenger flow data and obtained uncoupled intrinsic mode functions and a residual sequence after removing noisy data. Second, we built a CEEMDAN-IPSO-LSTM passenger flow prediction model for each decomposed component and extracted prediction values. Third, the experimental results showed that compared with the single LSTM model, CEEMDAN-IPSO-LSTM model reduced by 40 persons/35 persons, 44 persons/35 persons, 37 persons/31 persons, and 46.89%/35.1% in SD, RMSE, MAE, and MAPE, and increase by 2.32%/3.63% and 2.19%/1.67% in R and R2, respectively. This model can reduce the risks of public health security due to excessive crowding of passengers (especially in the period of COVID-19), as well as reduce the negative impact on the environment through the optimization of traffic flows, and develop low-carbon transportation.

A multi-scale forecasting model for CPI based on independent component analysis and non-linear autoregressive neural network

According to excellent performance on forecasting future trends based on historical observations, time series prediction has attracted considerable attention of many researchers. Consumer price index (CPI), as one of the indispensable indicators of macro economy, occupies a significant position in the national economic system. Arguably, the nonlinearity and nonstationarity of CPI series lead to poor accuracy of traditional prediction methods, from which the improvement of prediction performance is perceived as a challenging task. In this paper, a multi-scale forecasting technique for CPI is proposed, which is the hybrid of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), hierarchical agglomerative clustering (HAC), independent component analysis (ICA) and non-linear autoregressive (NAR) neural network. Firstly, the original time series is decomposed into several adaptive modes via the CEEMDAN. Secondly, HAC is introduced to cluster the modes according to their similarity aiming at performing dimensionality reduction. Thirdly, ICA is utilized to separate independent components (ICs) which implies the hidden information of time series. Finally, NAR neural network is applied to the ICs to obtain the forecasting sequences, and the final forecasting result is the combination of these individual forecasts. Comparative experiment results verify the superiority of the proposed hybrid methodology from the aspect of the performance criterion in CPI forecasting.

Research on precipitation prediction based on a complete ensemble empirical mode decomposition with adaptive noise–long short-term memory coupled model

Abstract Scientific precipitation predicting is of great value and guidance to regional water resources development and utilization, agricultural production, and drought and flood control. Precipitation is a nonlinear, non-smooth time series with significant stochasticity and uncertainty. In this paper, a complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) with long short-term memory (LSTM) model is developed for predicting annual precipitation in Zhengzhou city, China, which is compared with a single LSTM model, an ensemble empirical mode decomposition–LSTM model, a complementary ensemble empirical mode decomposition–LSTM model, and a CEEMDAN–autoregressive integrated moving average and a CEEMDAN–recurrent neural network model. The results show that the mean absolute percentage error, root mean square error, and coefficient of determination of the coupled CEEMDAN–LSTM model are 2.69%, 17.37 mm, and 0.9863, respectively. The prediction accuracy is significantly higher than that of the other five models, indicating that the proposed model has high prediction accuracy and can be used for annual precipitation forecasting in Zhengzhou city.

Short-term motion prediction of a semi-submersible by combining LSTM neural network and different signal decomposition methods

Reliable short-term motion prediction can improve the safety of marine operations. In order to improve the prediction accuracy, the original motion signal is often decomposed into several uncoupled sub-series by using the empirical mode decomposition (EMD) technology. Based on the traditional EMD method, several new signal decomposition methods have been proposed, such as ensemble empirical mode decomposition (EEMD), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and empirical wavelet transform (EWT). However, there is still a lack of detailed comparative study research on the influence of different signal decomposition methods on the prediction accuracy of short-term motion. In this study, several hybrid prediction models for short-term motion of semi-submersible are established by combining different signal decomposition methods and the LSTM model, namely EMD-LSTM, EEMD-LSTM, CEEMDAN-LSTM and EWT-LSTM models. Through the comparative analysis of the prediction results of different models, the influence of different signal decomposition methods on the prediction accuracy of short-term motion is investigated. It is found that the EWT-LSTM model consumes the least computing resource, but presents the best performance among different hybrid models.

An Non-Intrusive Load Event Detection Approach Based on CEEMDAN-WTD-F Test

<p>To improve the perception of the switch state of the electrical equipment and realize the identification of the non-intrusive load switching process more accurately, a non-intrusive load event detection method based on the CEEMDAN-WTD-F test is proposed in this paper. Firstly, the adaptive median filter is used to reduce the noise fluctuation of power data of electric equipment and the discrete sequence derivative is used to reduce turn-on transition time for individual loads. Then, based on the principle of decomposition denoising and statistical testing, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Wavelet Threshold De-noising (WTD) are used to process the input signal, and then the F test is used to determine whether an event occurs. Then, the proposed detection method is verified on the BLUED public data set, and a comparison experiment is carried out with the existing detection algorithm. The experimental results fully show that the proposed method has a lower missed detection rate and false detection rate than other detection algorithms, and the detection precision rate, recall rate, and F1 value are higher than 97%.</p> <p> </p>

A Hybrid Model for China’s Soybean Spot Price Prediction by Integrating CEEMDAN with Fuzzy Entropy Clustering and CNN-GRU-Attention

China’s soybean spot price has historically been highly volatile due to the combined effects of long-term massive import dependence and intricate policies, as well as inherent environmental elements. The accurate prediction of the price is crucial for reducing the amount of soybean-linked risks worldwide and valuable for the long-term sustainability of global agriculture. Therefore, a hybrid prediction model that combines component clustering and a neural network with an attention mechanism has been developed. After fully integrated complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) processing of the price series, the fuzzy entropy of each component is measured as the complexity characteristic. K-means clustering and reconstruction are applied to the components before being input to the CNN-GRU-Attention network for prediction to improve the model ability and adaptability of the sequences. In the empirical analysis, the proposed model outperforms other decomposition techniques and machine learning algorithms regarding prediction accuracy. After applying the decomposition part, the results have RMSE, MAPE, and MAE values of 49.59%, 22.58%, and 21.99% lower than those of the individual prediction part, respectively. This research presents a novel approach for market participants in the soybean industry for risk response. It gives a new perspective on agricultural product prices in sustainable agricultural marketing, while also providing practical tools for developing public policies and decision-making.

Flood forecasting and uncertainty analysis based on the combination of improved adaptive noise learning model and density estimation

AbstractThe strong randomness exhibited by runoff series means the accuracy of flood forecasting still needs to be improved. Mode mixing can be dealt with using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and the endpoint effect of CEEMDAN can be successfully dealt with using the mutual information criterion. To increase the computational effectiveness of broad learning (BL), orthogonal triangular matrix decomposition (QR) was used. A novel improved coupled CEEMDAN-QRBL flood forecasting model was created and applied to the prediction of daily runoff in Xiaolangdi Reservoir based on the benefit of quick calculation by the model output layer. The findings indicate that the enhanced QRBL is 28.92% more computationally efficient than the BL model, and that the reconstruction error of CEEMDAN has been decreased by 48.22%. The MAE of the improved CEEMDAN-QRBL model is reduced by 12.36% and 16.31%, and the Ens is improved by 8.81% and 3.96%, respectively, when compared with the EMD-LSTM and CEEMDAN-GRU models. The predicted values of the CEEMDAN-QRBL model have a suitable fluctuation range thanks to the use of nonparametric kernel density estimation (NPKDE), which might serve as a useful benchmark for the distribution of regional water resources.

An Integrated Framework Based on an Improved Gaussian Process Regression and Decomposition Technique for Hourly Solar Radiation Forecasting

The precise forecast of solar radiation is exceptionally imperative for the steady operation and logical administration of a photovoltaic control plant. This study proposes a hybrid framework (CBP) based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), an enhanced Gaussian process regression with a newly designed physical-based combined kernel function (PGPR), and the backtracking search optimization algorithm (BSA) for solar radiation forecasting. In the CEEMDAN-BSA-PGPR (CBP) model, (1) the CEEMDAN is executed to divide the raw solar radiation into a few sub-modes; (2) PACF (partial autocorrelation coefficient function) is carried out to pick the appropriate input variables; (3) PGPR is constructed to predict each subcomponent, respectively, with hyperparameters optimized by BSA; (4) the final forecasting result is produced by combining the forecasted sub-modes. Four hourly solar radiation datasets of Australia are introduced for comprehensive analysis and several models available in the literature are established for multi-step ahead prediction to demonstrate the superiority of the CBP model. Comprehensive comparisons with the other nine models reveal the efficacy of the CBP model and the superb impact of CEEMDAN blended with the BSA, respectively. The CBP model can produce more precise results compared with the involved models for all cases using different datasets and prediction horizons. Moreover, the CBP model is less complicated to set up and affords extra decision-making information regarding forecasting uncertainty.

Hybrid model for daily runoff interval predictions based on Bayesian inference

The highly nonlinear and nonstationary nature of runoff events in changing environments makes accurate and reliable runoff forecasting difficult. We propose a hybrid model by integrating an autoregressive model, Bayesian inference, a complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm, Bayesian optimization, and support vector regression. Two Bayesian inference methods (the No-U-Turn Sampler (NUTS) and variational inference) were used to calculate the parameters of the AR model to obtain a Bayesian AR (BAR) model. Credible intervals were used to analyze the uncertainty of the parameters and model prediction results. The above model is applied to the daily runoff predictions of hydrological stations in the Yellow River Basin of China. The results show that (1) the hybrid model can improve the prediction accuracy and (2) the NUTS algorithm-based model provides a narrower reliable interval and performs better in uncertainty analyses.

Interference fringe suppression in tunable diode laser absorption spectroscopy based on CEEMDAN-WTD

Owing to interference fringes in the multireflective gas cell, the detection sensitivity of a system in tunable diode laser absorption spectroscopy (TDLAS) will decrease significantly. In this work, a combined scheme of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and wavelet threshold denoising (WTD) is proposed. Theoretical simulations were performed to validate the effects of the proposed algorithm, which was also verified via a CO2 detection experiment. After CEEMDAN-WTD processing, the noisy intrinsic mode function (IMF), pure IMF, and residual components of the detection signal were identified and reconstructed successfully. Based on analysis of the simulations, CEEMDAN-WTD algorithm improved the signal-to-noise ratio by 1.87 times and decreased the root mean-squared error by 37.6% than the moving average algorithm. For the CO2 detection system, R2 = 0.999 was determined by the calibration experiment. Additionally, based on Allan variance analysis and a long-time experiment, the limit of detection was estimated to be 3.08 ppm for an average time of 148 s and measurement accuracy of 0.65%, respectively. The obtained results sufficiently validate that the CEEMDAN-WTD algorithm can effectively suppress interference fringe noise in TDLAS.