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Despite the widespread use of univariate empirical mode decomposition (EMD) in financial market forecasting, the application of multivariate empirical mode decomposition (MEMD) has not been fully investigated. The purpose of this study is to forecast the stock price index more accurately, relying on the capability of MEMD in modeling the dependency between relevant variables.

Quantitative and comprehensive assessments were carried out to compare the performance of some selected models. Data for the assessments were collected from three major stock exchanges, namely, the standard and poor 500 index from the USA, the Hang Seng index from Hong Kong and the Shanghai Stock Exchange composite index from China. MEMD-based support vector regression (SVR) was used as the modeling framework, where MEMD was first introduced to simultaneously decompose the relevant covariates, including the opening price, the highest price, the lowest price, the closing price and the trading volume of a stock price index. Then, SVR was used to set up forecasting models for each component decomposed and another SVR model was used to generate the final forecast based on the forecasts of each component. This paper named this the MEMD-SVR-SVR model.

The results show that the MEMD-based modeling framework outperforms other selected competing models. As per the models using MEMD, the MEMD-SVR-SVR model excels in terms of prediction accuracy across the various data sets.

This research extends the literature of EMD-based univariate models by considering the scenario of multiple variables for improving forecasting accuracy and simplifying computability, which contributes to the analytics pool for the financial analysis community.

Popular regression methodologies are inapplicable to obtain accurate metamodels for high dimensional practical problems since the computational time increases exponentially as the number of dimensions rises. The purpose of this paper is to use support vector regression with high dimensional model representation (SVR-HDMR) model to obtain accurate metamodels for high dimensional problems with a few sampling points.

High-dimensional model representation (HDMR) is a general set of quantitative model assessment and analysis tools for improving the efficiency of deducing high dimensional input-output system behavior. Support vector regression (SVR) method can approximate the underlying functions with a small subset of sample points. Dividing Rectangles (DIRECT) algorithm is a deterministic sampling method.

This paper proposes a new form of HDMR by integrating the SVR, termed as SVR-HDMR. And an intelligent sampling strategy, namely, DIRECT method, is adopted to improve the efficiency of SVR-HDMR.

Compared to other metamodeling techniques, the accuracy and efficiency of SVR-HDMR were significantly improved. The SVR-HDMR helped engineers understand the essence of underlying problems visually.

Machine learning methods have recently gained attention in business applications. We will explore the suitability of machine learning methods, particularly support vector regression (SVR) and radial basis function (RBF) approximation, in forecasting company sales. We compare the one-step-ahead forecast accuracy of these machine learning methods with traditional statistical forecasting techniques such as moving average (MA), exponential smoothing, and linear and quadratic trend regression on quarterly sales data of 43 Fortune 500 companies. Moreover, we implement an additive seasonal adjustment procedure on the quarterly sales data of 28 of the Fortune 500 companies whose time series exhibited seasonality, referred to as the seasonal group. Furthermore, we prove a mathematical property of this seasonal adjustment procedure that is useful in interpreting the resulting time series model. Our results show that the Gaussian form of a moving RBF model, with or without seasonal adjustment, is a promising method for forecasting company sales. In particular, the moving RBF-Gaussian model with seasonal adjustment yields generally better mean absolute percentage error (MAPE) values than the other methods on the sales data of 28 companies in the seasonal group. In addition, it is competitive with single exponential smoothing and better than the other methods on the sales data of the other 15 companies in the non-seasonal group.

The purpose of this study is to forecast the development performance of important economies in a smart city using mixed-frequency data.

This study introduces reverse unrestricted mixed-data sampling (RUMIDAS) to support vector regression (SVR) to develop a novel RUMIDAS-SVR model. The RUMIDAS-SVR model was estimated using a quadratic programming problem. The authors then use the novel RUMIDAS-SVR model to forecast the development performance of all high-tech listed companies, an important sector of the economy reflecting the potential and dynamism of urban economic development in Shanghai using the mixed-frequency consumer price index (CPI) producer price index (PPI), and consumer confidence index (CCI) as predictors.

The empirical results show that the established RUMIDAS-SVR is superior to the competing models with regard to mean absolute error (MAE) and root-mean-squared error (RMSE) and multi-source macroeconomic predictors contribute to the development performance forecast of important economies.

Smart city policy makers should create a favourable macroeconomic environment, such as controlling inflation or stabilising prices for companies within the city, and companies within the important city economic sectors should take initiative to shoulder their responsibility to support the construction of the smart city.

This study contributes to smart city monitoring by proposing and developing a new model, RUMIDAS-SVR, to help the construction of smart cities. It also empirically provides strategic insights for smart city stakeholders.

The purpose of this paper is to assess the quality of commercial lubricant oils. A spectroscopic method was used in combination with multivariate regression techniques (ordinary multivariate multiple regression, principal components analysis, partial least squares, and support vector regression (SVR)).

The rationale behind the use of SVR was the fuzzy characteristics of the signal and its inherent ability to find nonlinear, global solutions in highly complex dimensional input spaces. Thus, SVR allows extracting useful information from calibration samples that makes it possible to characterize physical-chemical properties of the lubricant oils.

A dataset of 42 spectra measured from oil standards was studied to assess the concentration of copper into the oils and, thus, evaluate the wearing of the machinery. It was found that the use of SVR was very advantageous to get a regression model.

The use of genetic algorithms coupled to SVR was considered in order to reduce the time needed to find the optimal parameters required to get a suitable prediction model.

Knowledge of the effect of various cash-flow factors on expected project profit is important to effectively manage productivity on construction projects. This study was conducted to develop and test the sensitivity of a Machine Learning Support Vector Regression Algorithm (SVRA) to predict construction project profit in Ghana.

The study relied on data from 150 institutional projects executed within the past five years (2014–2018) in developing the model. Eighty percent (80%) of the data from the 150 projects was used at hyperparameter selection and final training phases of the model development and the remaining 20% for model testing. Using MATLAB for Support Vector Regression, the parameters available for tuning were the epsilon values, the kernel scale, the box constraint and standardisations. The sensitivity index was computed to determine the degree to which the independent variables impact the dependent variable.

The developed model's predictions perfectly fitted the data and explained all the variability of the response data around its mean. Average predictive accuracy of 73.66% was achieved with all the variables on the different projects in validation. The developed SVR model was sensitive to labour and loan.

The developed SVRA combines variation, defective works and labour with other financial constraints, which have been the variables used in previous studies. It will aid contractors in predicting profit on completion at commencement and also provide information on the effect of changes to cash-flow factors on profit.

Nitrogen oxides (NO_x) have been considered as primarily responsible for many serious environmental problems. Removing NO is the key task to remove NO_x hazards. To clarify, NO removal process for pitch-based spherical-activated carbons (PSACs), an online prediction and optimization technique in real-time based on support vector machine algorithm in regression (support vector regression [SVR]) is discussed. The purpose of this paper is to develop a predictor and optimizer system on selective catalytic reduction of NO (SCRN) using experimental data and data-driven SVR intelligence methods.

Predictor and optimizer using developed SVR have been proposed. To modify the training efficiency of SVR, the authors especially customize batch normalization and k-fold cross-validation techniques according to the unique characteristics of PSACs model.

The results present that SVR provides a property regression model since it can linkage linear and non-linear process and property relationships in few experimental data sets. Also, the integrated normalization and k-fold cross-validation show a satisfying improvement and results for SVR optimization. The predicted results of predictor and optimizer in single and double factor systems are in excellent agreement with the experimental data.

SCRN-PO for predicting and optimization SCRN problems is developed by data-driven methods. The outperformed SCRN-PO system is used to predict multiple-factors property parameters and obtain optimum technological parameters in real-time. Also, experiment duration is greatly shortened.

The multisource uncertainties, including material dispersion, load fluctuation and geometrical tolerance, have crucial effects on fatigue performance of turbine bladed disks. In view of the aim of this paper, it is essential to develop an advanced approach to efficiently quantify their influences and evaluate the fatigue life of turbine bladed disks.

In this study, a novel combined machine learning strategy is performed to fatigue assessment of turbine bladed disks. Proposed model consists of two modeling phases in terms of response surface method (RSM) and support vector regression (SVR), namely RSM-SVR. Two different input sets obtained from basic variables were used as the inputs of RSM, then the predicted results by RSM in first phase is used as inputs of SVR model by using a group data-handling strategy. By this way, the nonlinear flexibility of SVR inputs is improved and RSM-SVR model presents the high-tendency and efficiency characteristics.

The accuracy and tendency of the RSM-SVR model, applied to the fatigue life estimation of turbine bladed disks, are validated. The results indicate that the proposed model is capable of accurately simulating the nonlinear response of turbine bladed disks under multisource uncertainties, and SVR-RSM model provides an accurate prediction strategy compared to RSM and SVR for fatigue analysis of complex structures.

The results indicate that the proposed model is capable of accurately simulate the nonlinear response of turbine bladed disks under multisource uncertainties, and SVR-RSM model provides an accurate prediction compared to RSM and SVRE for fatigue analysis of turbine bladed disk.

The purpose of this paper is to show a comparative study of different direction-of-arrival (DOA) estimation techniques, namely, multiple signal classification (MUSIC) algorithm, delay-and-sum (DAS) beamforming, support vector regression (SVR), multivariate linear regression (MLR) and multivariate curvilinear regression (MCR).

The relative delay between the microphone signals is the key attribute for the implementation of any of these techniques. The machine-learning models SVR, MLR and MCR have been trained using correlation coefficient as the feature set. However, MUSIC uses noise subspace of the covariance-matrix of the signals recorded with the microphone, whereas DAS uses the constructive and destructive interference of the microphone signals.

Variations in root mean square angular error (RMSAE) values are plotted using different DOA estimation techniques at different signal-to-noise-ratio (SNR) values as 10, 14, 18, 22 and 26dB. The RMSAE curve for DAS seems to be smooth as compared to PR1, PR2 and RR but it shows a relatively higher RMSAE at higher SNR. As compared to (DAS, PR1, PR2 and RR), SVR has the lowest RMSAE such that the graph is more suppressed towards the bottom.

DAS has a smooth curve but has higher RMSAE at higher SNR values. All the techniques show a higher RMSAE at the end-fire, i.e. angles near 90°, but comparatively, MUSIC has the lowest RMSAE near the end-fire, supporting the claim that MUSIC outperforms all other algorithms considered.

The purpose is to analyze and discuss the sustainable development (SD) and financing risk assessment (FRA) of resource-based industrial clusters under the Internet of Things (IoT) economy and promote the application of Machine Learning methods and intelligent optimization algorithms in FRA.

This study used the Support Vector Machine (SVM) algorithm that is analyzed together with the Genetic Algorithm (GA) and Ant Colony Optimization (ACO) algorithm. First, Yulin City in Shaanxi Province is selected for case analysis. Then, resource-based industrial clusters are studied, and an SD early-warning model is implemented. Then, the financing Risk Assessment Index System is established from the perspective of construction-operation-transfer. Finally, the risk assessment results of Support Vector Regression (SVR) and ACO-based SVR (ACO-SVR) are analyzed.

The results show that the overall sustainability of resource-based industrial clusters and IoT industrial clusters is good in the Yulin City of Shaanxi Province, and the early warning model of GA-based SVR (GA-SVR) has been achieved good results. Yulin City shows an excellent SD momentum in the resource-based industrial cluster, but there are still some risks. Therefore, it is necessary to promote the industrial structure of SD and improve the stability of the resource-based industrial cluster for Yulin City.

The results can provide a direction for the research on the early warning and evaluation of the SD-oriented resource-based industrial clusters and the IoT industrial clusters, promoting the application of SVM technology in the engineering field.