Data Decomposition Method Research Articles

Double decomposition with enhanced least-squares support vector machine to predict water level

ABSTRACT As global climates undergo changes, the frequency of water-related disasters rises, leading to significant economic losses and safety hazards. During flood events, river water levels exhibit unpredictable fluctuations, introducing considerable noise that poses challenges for accurate prediction. A prediction of water level by using existing water level data makes a major contribution to forecasting flood. Enhanced least-squares support vector machine (ELSSVM) is utilized by integrating an additional extra bias error control term. In this study, least-squares support vector machine (LSSVM) and ELSSVM optimized by the genetic algorithm (GA) were chosen to be compared with the help of data decomposition methods to improve daily water level prediction accuracy. Double empirical mode decomposition (DEMD) will be integrated with LSSVM and ELSSVM. Thus, the models are named LSSVM-GA, ELSSVM-GA, empirical mode decomposition (EMD)-LSSVM-GA, EMD-ELSSVM-GA, DEMD-LSSVM-GA, and DEMD-ELSSVM-GA. The proposed models are used in forecasting the water level of Klang River in Sri Muda, Malaysia. The behavior proposed models are investigated and compared based on several performance metrics. The results demonstrated that the DEMD-ELSSVM-GA model outperformed the other models based on the performance analysis in forecasting the water level with RMSE = 0.2536 m and R2 = 0.8596 for testing data that indicate the forecasting accuracy.

Hybrid machine learning models for groundwater level prediction in a snow‐dominated region: An evaluation of EEMD, VMD and EWT decomposition techniques

Hybrid machine learning models for groundwater level prediction in a <scp>snow‐dominated</scp> region: An evaluation of <scp>EEMD</scp>, <scp>VMD</scp> and <scp>EWT</scp> decomposition techniques

Analysis of EEG signals and data acquisition methods: a review

ABSTRACT Early illness diagnosis and prediction are important goals in healthcare in order to offer timely preventive measures. The best, least invasive, and most reliable way for identifying any neurological disorder is EEG analysis. If neurological disorders could somehow be predicted in advance, patients could be saved from their detrimental consequences. With promising new advancements in machine learning-based algorithms, Early and precise prediction might induce a radical shift. Here, we present a thorough analysis of cutting-edge AI methods for exploiting EEG data for Parkinson’s disease early warning symptoms detection, sleep apnoea, drowsiness, schizophrenia, motor imagery classification, and emotion recognition, among other conditions. All of the EEG signal analysis procedures used by different authors, such as hardware software data sets, channel, frequency, epoch, preprocessing, decomposition method, features, and classification, have been compared and analysed in detail. We will point out the difficulties, gaps and limitations in the current research and suggest future avenues of research.

An interval AQI combination prediction model based on multiple data decomposition and information aggregation operator.

In this paper, an interval Air Quality Index (AQI) combination prediction model based on EEMD, VMD, and the weighted power average (WPA) operator is proposed. EEMD and VMD decompose complex AQI data effectively, while WPA operator reasonably aggregates the prediction results of different models. We validate the effectiveness of the proposed model using Shenzhen's daily interval AQI. Furthermore, three kinds of prediction models are compared with the proposed model to highlight its advantages from various perspectives. The results show that the introduction of data decomposition methods significantly improves the model's prediction accuracy, WPA operator further enhances the model's prediction capability, and the incorporation of EEMD and VMD enables the proposed model to have stronger feature extraction capabilities for complex time series. As a result, the model proposed in this paper demonstrates strong generalization ability and prediction accuracy, making it applicable not only for air quality prediction but also for other domains such as economics and environment.

Deep learning method for predicting electromagnetic emission spectrum of aerospace equipment

AbstractThis paper proposes a deep learning method to predict the electromagnetic emission spectrum in the electromagnetic compatibility (EMC) test of aerospace products. A threshold‐based data decomposition method is used to propose the spike signal, reconstruct the original test data, and solve the contradiction between the overfitting and prediction accuracy of the deep learning method to deal with the EMC test spectrum. Using a long short‐term memory neural network architecture for predicting electromagnetic emission spectrum, the Bayesian optimization method is used to optimize the network hyperparameter, and the acquisition function is introduced into the loss function to improve the comprehensive training optimization of deep learning network. Apply the method to three numerical examples: signal line current conduction emission, power line voltage conduction emission, and electric field radiation emission. The analysis results indicate that at a 95% confidence level, the predicted electromagnetic emission spectrum is basically consistent with the test results. The prediction results can be used as the basis for EMC evaluation of aerospace electronic equipment.

A Novel Hybrid Model Combining Improved VMD and ELM with Extended Maximum Correntropy Criterion for Prediction of Dissolved Gas in Power Transformer Oil

The prediction of dissolved gas change trends in power transformer oil is very important for the diagnosis of transformer faults and ensuring its safe operation. Considering the time series and nonlinear features of the gas change trend, this paper proposes a novel robust extreme learning machine (ELM) model combining an improved data decomposition method for gas content forecasting. Firstly, the original data with nonlinear and sudden change properties will make the forecasting model unstable, and thus an improved variational modal decomposition (IPVMD) method is developed to decompose the original data to obtain the multiple modal dataset, in which the marine predators algorithm (MPA) optimization method is utilized to optimize the free parameters of the VMD. Second, the ELM as an efficient and easily implemented tool is used as the basic model for dissolved gas forecasting. However, the traditional ELM with mean square error (MSE) criterion is sensitive to the non-Gaussian measurement noise (or outliers). In addition, considering the nonlinear non-Gaussian properties of the dissolved gas, a new learning criterion, called extended maximum correntropy criterion (ExMCC), is defined by using an extended kernel function in the correntropy framework, and the ExMCC as a learning criterion is introduced into the ELM to develop a novel robust regression model (called ExMCC-ELM) to improve the ability of ELM to process mutational data. Third, a gas-in-oil prediction scheme is proposed by using the ExMCC-ELM performed on each modal obtained by the proposed IPVMD. Finally, we conducted several simulation studies on the measured data, and the results show that the proposed method has good predictive performance.

Constructing Attention‐LSTM‐VAE Power Load Model Based on Multiple Features

With the complexity of modern power system and the susceptibility to external weather influences, it brings challenges to build an accurate load model. This paper proposes a variational autoencoder (VAE) long short‐term memory (LSTM) load model based on the attention mechanism (Attention). First, the Prophet data decomposition method is used to decompose long sequences of load data at multiple time scales. Second, the correlation‐based feature selection with maximum information coefficient (CFS‐MIC) method is employed to select weather features based on their relevance, a subset of features with high correlation and low redundancy is chosen as model inputs. Finally, the Attention‐LSTM‐VAE model is constructed to capture the temporal variations laws of load. The dataset includes 2 years of load values and weather data collected in Caojiaping, Hunan Province, China. The experimental results show that the Attention‐LSTM‐VAE model has the lowest mean absolute error of 0.0374 and the highest R‐squared value of 0.9714, verifying the accuracy of the model. Therefore, the performance of the Attention‐LSTM‐VAE model is better than the general deep learning load models, which has important reference for the research of power load models. Comparisons with other deep learning methods, the experimental results show that the Attention‐LSTM‐VAE model has the lowest mean absolute error of 0.0374 and the highest R‐squared value of 0.9714. The Attention‐LSTM‐VAE has better robustness, stability, and accuracy in load modeling, which has an important reference for the research of power load models.

Application of forecasting strategies and techniques to natural gas consumption: A comprehensive review and comparative study

Accurate forecasting of natural gas consumption (NGC) plays an important role in energy supply, energy trading, economic effects and environmental sustainability. NGC forecasts can be used to adjust production and supply plans to improve gas efficiency and reduce carbon emissions and supply chain waste. This paper reviews the research progress on NGC in the past decade, analyzes the typical characteristics of different forecasting strategies, and highlights 163 studies in terms of the technical aspects of feature processing methods, data decomposition methods, forecasting models and optimization algorithms. It also systematically elaborates the application of statistical models, machine learning models, grey models, logistic regression and their combinations in predictive models. Bibliometric methods are also utilized to dissect research hotspots and summarize cutting-edge trends in the field. It is worth mentioning that in the terms of hybrid model structures, the application and performance of various model structures are described and evaluated. In this paper, the future development is discussed from spatiotemporal characteristics, studying reasonable data decomposition layers and fusion models, considering potential data privacy issues, and developing artificial intelligence-supporting models and interpretable frameworks. This paper is expected to provide a multi-technology reference for natural gas forecasting and help researchers to select and develop more accurate forecasting techniques and strategies.

Prediction of Sea Level with Vertical Land Movement Correction Using Deep Learning

Sea level rise (SLR) in small island countries such as Kiribati and Tuvalu have been a significant issue for decades. There is an urgent need for more accurate and reliable scientific information regarding SLR and its trend and for more informed decision making. This study uses the tide gauge (TG) dataset obtained from locations in Betio, Kiribati and Funafuti, Tuvalu with sea level corrections for vertical land movement (VLM) at these locations from the data obtained by the Global Navigation Satellite System (GNSS) before the sea level trend and rise predictions. The oceanic feature inputs of water temperature, barometric pressure, wind speed, wind gust, wind direction, air temperature, and three significant lags of sea level are considered in this study for data modeling. A new data decomposition method, namely, successive variational mode decomposition (SVMD), is employed to extract intrinsic modes of each feature that are processed for selection by the Boruta random optimizer (BRO). The study develops a deep learning model, namely, stacked bidirectional long short-term memory (BiLSTM), to make sea level (target variable) predictions that are benchmarked by three other AI models adaptive boosting regressor (AdaBoost), support vector regression (SVR), and multilinear regression (MLR). With a comprehensive evaluation of performance metrics, stacked BiLSTM attains superior results of 0.994207, 0.994079, 0.988219, and 0.899868 for correlation coefficient, Wilmott’s Index, the Nash–Sutcliffe Index, and the Legates–McCabe Index, respectively, for Kiribati, and with values of 0.996806, 0.996272, 0.992316, and 0.919732 for correlation coefficient, Wilmott’s Index, the Nash–Sutcliffe Index, and the Legates–McCabe Index, respectively, for the case of Tuvalu. It also shows the lowest error metrics in prediction for both study locations. Finally, trend analysis and linear projection are provided with the GNSS-VLM-corrected sea level average for the period 2001 to 2040. The analysis shows an average sea level rate rise of 2.1 mm/yr for Kiribati and 3.9 mm/yr for Tuvalu. It is estimated that Kiribati and Tuvalu will have a rise of 80 mm and 150 mm, respectively, by the year 2040 if estimated from year 2001 with the current trend.

Multivariate wind speed forecasting based on multi-objective feature selection approach and hybrid deep learning model

This study proposes an effective model for enhancing the short-term wind speed forecasting performance by considering the effect of multiple meteorological factors. (a) The filter-wrapper non-dominated sorting differential evolution algorithm incorporating K-medoid clustering (FWNSDEC) is designed to select key meteorological factors and generate multiple feature subsets. For each feature subset, the hybrid deep learning model is designed: (b) singular spectrum analysis (SSA) is used to decompose the meteorological factors and construct the three-dimensional input structure; (c) convolutional long short-term memory (ConvLSTM) network is then adopted to process the sample set of three-dimensional sequence, and the final forecasting result is the average prediction of all the built ConvLSTMs. To evaluate the effectiveness of FWNSDEC-SSA-ConvLSTM, three comparative experiments on four datasets collected from the National Renewable Energy Laboratory are implemented. Experiment results show that the average mean absolute percentage error over four datasets for 1-step-ahead, 2-step-ahead, and 3-step-ahead forecasting is 1.42%, 1.99%, and 3.28%, respectively, which are much better than the predictions using feature selection benchmarks, hybrid forecasting benchmarks with different deep learning networks and data decomposition methods, and other advanced forecasting systems. Extended model discussion in terms of Friedman test and parameters sensitivity analysis further verifies the potential of proposed model.

Volatility index prediction based on a hybrid deep learning system with multi-objective optimization and mode decomposition

Advances in volatility index prediction based on computational intelligence have brought wide-ranging benefits to financial risk management. However, current studies in the field remain limited and need further improvement due to these research gaps: (1) ignoring the important role of probabilistic prediction in characterizing uncertainty risks; (2) relying on single-objective optimization algorithm to optimize prediction model, thereby ignoring the advantages of multi-objective optimization; (3) emphasizing nonlinear modeling, not considering both nonlinear and linear modeling simultaneously. Aiming to address these gaps, a novel multi-objective hybrid deep learning system, composed of a modified multi-objective optimizer, a clockwork recurrent neural network, and an improved mode decomposition method, is newly proposed to perform deterministic and probabilistic volatility index prediction. Concretely, the volatility index is decomposed into some modes using an advanced data decomposition method; further, the clockwork recurrent neural network is considered a prediction engine to model these modes, which has an excellent ability to model the long-term dependency for linear and nonlinear time series depending on its mechanism of temporal granularity, as compared to traditional recurrent neural networks; finally, the prediction results can be obtained by integrating the predictions from these modes, using the mode weights calculated by an improved multi-objective optimizer with the objectives of prediction accuracy and stability. To validate the performance of the proposed hybrid deep learning system, case studies and corresponding sensitivity and convergence analyses are carried out. From the perspective of the indicator mean absolute percentage error, the maximum improvements of our proposed system reach 67.50%, 75.82%, and 75.42% in Case I, Case II, and Case III, respectively, thus indicating its superiority and practical feasibility.

Predicting ammonia nitrogen in surface water by a new attention-based deep learning hybrid model

Ammonia nitrogen (NH3–N) is closely related to the occurrence of cyanobacterial blooms and destruction of surface water ecosystems, and thus it is of great significance to develop predictive models for NH3–N. However, traditional models cannot fully consider the complex nonlinear relationship between NH3–N and various relative environmental parameters. The long short-term memory (LSTM) neural network can overcome this limitation. A new hybrid model BC-MODWT-DA-LSTM was proposed based on LSTM combining with the dual-stage attention (DA) mechanism and boundary corrected maximal overlap discrete wavelet transform (BC-MODWT) data decomposition method. By introducing attention mechanism, LSTM could selectively focus on the input data. BC-MODWT could decompose the input data into sublayers to determine the main swings and trends of the input feature series. The BC-MODWT-DA-LSTM hybrid model was superior to other studied models with lower average prediction errors. It could maintain NASH Sutcliffe efficiency coefficient (NSE) values above 0.900 under the lead time up to 7 days, and the area under the receiver operating characteristic (ROC) curve could reach 0.992. The hybrid model also had higher prediction accuracies at the peak spots, indicating that it was capable of early warning when sudden high NH3–N pollution occurred. The high forecasting accuracy of the suggested hybrid method proved that further improving LSTM model without introducing more complex topologies was a promising water quality prediction method.

PM2.5 forecasting for an urban area based on deep learning and decomposition method

Rapid growth in industrialization and urbanization have resulted in high concentration of air pollutants in the environment and thus causing severe air pollution. Excessive emission of particulate matter to ambient air has negatively impacted the health and well-being of human society. Therefore, accurate forecasting of air pollutant concentration is crucial to mitigate the associated health risk. This study aims to predict the hourly PM2.5 concentration for an urban area in Malaysia using a hybrid deep learning model. Ensemble empirical mode decomposition (EEMD) was employed to decompose the original sequence data of particulate matter into several subseries. Long short-term memory (LSTM) was used to individually forecast the decomposed subseries considering the influence of air pollutant parameters for 1-h ahead forecasting. Then, the outputs of each forecast were aggregated to obtain the final forecasting of PM2.5 concentration. This study utilized two air quality datasets from two monitoring stations to validate the performance of proposed hybrid EEMD-LSTM model based on various data distributions. The spatial and temporal correlation for the proposed dataset were analysed to determine the significant input parameters for the forecasting model. The LSTM architecture consists of two LSTM layers and the data decomposition method is added in the data pre-processing stage to improve the forecasting accuracy. Finally, a comparison analysis was conducted to compare the performance of the proposed model with other deep learning models. The results illustrated that EEMD-LSTM yielded the highest accuracy results among other deep learning models, and the hybrid forecasting model was proved to have superior performance as compared to individual models.

Hybrid machine learning models for predicting short-term wave energy flux

Wave energy flux is a critical index of available wave energy in a region. The short-term wave energy flux prediction is conducive to implementing marine energy generation management. Due to the high degree of nonlinearity in the time series, it is challenging to achieve the accurate prediction of the short-term wave energy flux. The existing prediction models usually only focus on the prediction accuracy without considering the prediction stability. This paper presents hybrid support vector models combining a multi-objective optimizer and data denoising for short-term wave energy flux prediction. The proposed models are tested on the hourly wave energy flux data from November 2014 to January 2015 at the South Energy Test Site in the United States. The results indicate that: (1) The model combining ensemble empirical mode decomposition with multi-objective grey wolf optimizer and support vector machine has the highest prediction accuracy and stability in the case study. The mean absolute percentage error and standard deviation of the percent error are 1.95% and 2.46%, respectively; (2) The data decomposition method improves the radial basis function neural network's prediction accuracy and stability, but has a negative impact on some predictors, such as extreme gradient boosting and random forest; (3) The optimizer does not necessarily have a positive effect on the accuracy and stability of the predictor; (4) The prediction accuracy and stability of the multi-objective optimization hybrid model are higher than that of the single-objective optimization model.

Partial Discharge (PD) Signal Detection and Isolation on High Voltage Equipment Using Improved Complete EEMD Method

Electricity has a crucial function in contemporary civilization. The power grid must be stable to ensure the efficiency and dependability of electrical equipment. This implies that the high-voltage equipment at the substation must be reliably operated. As a result, the appropriate and dependable use of systems to monitor the operating status of high-voltage electrical equipment has recently gained attention. Partial discharge (PD) analysis is one of the most promising solutions for monitoring and diagnosing potential problems in insulation systems. Noise is a major challenge in diagnosing and detecting defects when using this measurement. This study aims to denoise PD signals using a data decomposition method, improved complete ensemble empirical mode decomposition with adaptive noise algorithm, combined with statistical significance test to increase noise reduction efficiency and to derive and visualize the Hilbert spectrum of the input signal in time-frequency domain after filtering the noise. In the PD signal analysis, both artificial and experimental signals were used as input signals in the decomposition method. For these signals, this study has yielded significant improvement in the denoising and the PD detecting process indicated by statistical measures. Thus, the signal decomposition by using the proposed method is proven to be a useful tool for diagnosing the PD on high voltage equipment.

Ensemble wind speed forecasting system based on optimal model adaptive selection strategy: Case study in China

Under the vision of global carbon neutrality and peaking, the vigorous development of easily accessible renewable energy sources, such as wind energy, is extremely important. However, achieving accurate and reliable forecasts remains a challenging problem owing to the irregularity and volatility of wind speed data. Existing forecasting modules based on point prediction ignore the limitations of a single forecasting algorithm, fail to capture the wind speed uncertainty in data or achieve high forecasting accuracy. To overcome these shortcomings, an ensemble system of decomposition, adaptive selection, and forecast was proposed to simulate the actual wind speed data of a wind farm. Empirical analyses show that an excellent denoising effect is achieved by the data decomposition method. Moreover, the adaptability of forecasting modules to complex data is improved by the optimal submodel selection strategy. The reduction coefficients are effectively optimized by the multi-objective bald eagle search algorithm used for the first time. This system not only improves the point forecasting accuracy but also analyzes the uncertainty of wind speed, providing actual technical support for grid dispatching.

Integrating data decomposition and machine learning methods: An empirical proposition and analysis for renewable energy generation forecasting

Renewable energy generation (REG) has been vigorously developed under the dual pressure of environmental contamination and fossil fuel energy shortage. Whereas the rapidly increasing REG instigates stochastic volatility may threaten the REG deployment and grid stability. Motivated by the grid flexibility enhancement depending on the expansion of grid infrastructure that belongs to the mid-term management domain, the precise predictions for mid-term, especially monthly REG sourced from diverse renewable energy, are indispensable for power plant configuration, electricity end-uses promotion, and grid expansion to propel the energy system transformation. To this end, aiming at diverse monthly REG datasets characterized by periodicity, nonlinearity, and volatility, the seasonal-trend decomposition procedure based on loess (STL) is initially employed to extract the trend and periodic features of monthly REG datasets, and generate trend, seasonal, and remainder subseries. Based on the decomposed data, long-short term memory (LSTM) is utilized for subseries prediction, and then the projections are integrated to compose the ultimate forecasted results for original REG observations. To validate the efficacy and adaptability of the proposed data-driven STL-LSTM framework, several machine learning methods and autoregressive models are involved in predicting practical monthly electricity generation datasets sourced by solar, wind, hydropower, and geothermal energy in different countries. The forecasted results indicate that the proposed framework is demonstrated with superlative prediction performance and strong adaptability for generation prediction derived from diverse renewable energy. Therefore, the novel STL-LSTM framework may constitute a promising alternative for REG forecasting.

Modal Decomposition Techniques: Application in Coherent Structures for a Saccular Aneurysm Model

Aneurysms are localized expansions of blood vessels which can be fatal upon rupture. Studies have shown that aneurysm flows exhibit complex flow phenomena which consist of single or multiple vortical structures that move within the flow cycle. Understanding the complex flow behaviors of aneurysms remain challenging. Thus, the goal of this study is to quantify the flow behavior and extract physical insights into aneurysm flows using advance data decomposition methods, Proper Orthogonal Decomposition (POD) and Dynamic Mode Decomposition (DMD). The velocity field data were obtained by performing 2D Particle Image Velocimetry (2D PIV) on the mid-plane of an idealized, rigid, saccular aneurysm model. The input flow conditions were set to Rep=50 and 150 for a fixed α=2 using a precisely controlled piston pump system. POD was used to quantify the spatial features of the flows, while DMD was used to obtain insight on the dynamics. The results obtained from POD and DMD showed the capability of both methods to quantify the flow field, with the modes obtained providing different insights into the flow evolution in the aneurysm. The curve-fitting step of the POD time-varying coefficients, and the appropriate selection of DMD modes based on their energy contribution, allowed the mathematical flow models from POD and DMD to reconstruct flow fields at any given time step. This can be used for validation of numerical or computational data.

A short-term wind speed prediction method utilizing novel hybrid deep learning algorithms to correct numerical weather forecasting

The accuracy of the wind speed prediction is of crucial significance for the operation and dispatch of the power grid system reasonably. However, wind speed is so random and intermittent that the accuracy of wind speed prediction always remains unsatisfactory. Moreover, the coupling relationship between other meteorological variables and wind speed in the time and frequency domains has rarely been studied. Subsequently, a hybrid wind speed prediction model based on weather research and forecasting (WRF) simulation is proposed according to a multivariate data decomposition method and deep learning algorithm optimized by an attention mechanism and a grid search algorithm. Firstly, the WRF simulation is utilized to obtain the predicted wind speed and other meteorological variables are also extracted from WRF different domains. Furthermore, the pearson correlation coefficient (PCC) method is adopted to select principal meteorological variables as the input series. Additionally, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method decomposes input series and historical data into respective intrinsic mode functions (IMFs). Then, a new hybrid deep learning model, combining a convolutional neural network (CNN) and a bidirectional long short-term memory network (BLSTM) optimized via an attention mechanism (AM) and a grid search method (GS), is proposed to predict the error and correct the wind speed from WRF innermost domain. Finally, the validation case study is conducted to verify the effectiveness of the proposed model. The results indicate that the proposed model outperforms other comparative models in terms of single-step and multi-step wind speed prediction. Specifically, the values of the mean absolute error (MAE), the mean absolute percentage error (MAPE), and the root mean square error (RMSE) are 0.1042 m/s, 4.63% and 0.1309 m/s after correction, decreased by 94.13%, 91.75% and 93.93%, respectively, compared to those without correction.

LIONirs: flexible Matlab toolbox for fNIRS data analysis

BackgroundFunctional near-infrared spectroscopy (fNIRS) is a suitable tool for recording brain function in pediatric or challenging populations. As with other neuroimaging techniques, the scientific community is engaged in an evolving debate regarding the most adequate methods for performing fNIRS data analyses. New methodWe introduce LIONirs, a neuroinformatics toolbox for fNIRS data analysis, designed to follow two main goals: (1) flexibility, to explore several methods in parallel and verify results using 3D visualization; (2) simplicity, to apply a defined processing pipeline to a large dataset of subjects by using the MATLAB Batch System and available on GitHub. ResultsWithin the graphical user interfaces (DisplayGUI), the user can reject noisy intervals and correct artifacts, while visualizing the topographical projection of the data onto the 3D head representation. Data decomposition methods are available for the identification of relevant signatures, such as brain responses or artifacts. Multimodal data recorded simultaneously to fNIRS, such as physiology, electroencephalography or audio-video, can be visualized using the DisplayGUI. The toolbox includes several functions that allow one to read, preprocess, and analyze fNIRS data, including task-based and functional connectivity measures. Comparison with existing methodsSeveral good neuroinformatics tools for fNIRS data analysis are currently available. None of them emphasize multimodal visualization of the data throughout the preprocessing steps and multidimensional decomposition, which are essential for understanding challenging data. Furthermore, LIONirs provides compatibility and complementarity with other existing tools by supporting common data format. ConclusionsLIONirs offers a flexible platform for basic and advanced fNIRS data analysis, shown through real experimental examples.