The volatility is related to financial risk and its prediction accuracy is very important in portfolio optimisation. A large body of literature to-date suggests Support Vector Machines (SVM) as the ...“best of regression” algorithms for financial data regression. Recent work however found that new deep learning––Long Short Term Memory Recurrent Neural Networks (LSTM RNNs) outperformed SVM for classification problems. In the present paper we conduct a new unbiased evaluation of these two modelling techniques for regression problems, and we also compare them with a popular regression model - Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model for financial volatility or risk forecasting. Our experiments using financial data show that the LSTM RNNs performed as good as v-SVR for large interval volatility forecasting and both performed much better than GARCH model for two financial indices (S&P 500 and AAPL). The LSTM RNNS deep learning method can learn from big raw data and can be run with many hidden layers and neurons under GPU to achieve a good prediction for long sequence data compared to the support vector regression. The deep learning technique - LSTM RNNs with big data can be used to improve the volatility prediction instead of v-SVR when the v-SVR does not predict well for some financial stocks of a portfolio. This will help investors to win the competition to maximize their profit.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•Surveys on recent applications of artificial intelligence techniques to rotating machinery fault diagnosis.•Provides a guidance of how to choose and use artificial intelligence techniques in ...engineering.•Describes the artificial intelligence techniques applications and rotating machinery fault diagnosis trends.
Fault diagnosis of rotating machinery plays a significant role for the reliability and safety of modern industrial systems. As an emerging field in industrial applications and an effective solution for fault recognition, artificial intelligence (AI) techniques have been receiving increasing attention from academia and industry. However, great challenges are met by the AI methods under the different real operating conditions. This paper attempts to present a comprehensive review of AI algorithms in rotating machinery fault diagnosis, from both the views of theory background and industrial applications. A brief introduction of different AI algorithms is presented first, including the following methods: k-nearest neighbour, naive Bayes, support vector machine, artificial neural network and deep learning. Then, a broad literature survey of these AI algorithms in industrial applications is given. Finally, the advantages, limitations, practical implications of different AI algorithms, as well as some new research trends, are discussed.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
•The ability of LSSVM, MARS and M5Tree is investigated in modeling pan evaporation.•LSSVM models outperformed the MARS and M5Tree models in estimating pan evaporation.•The models are also compared in ...two different cross station applications.•MARS performed better than the others when the local data are not available.•RMSE of LSSVM and M5Tree was respectively decreased by 11.4% and 18.4% using MARS.
Pan evaporation (Ep) modeling is an important issue in reservoir management, regional water resources planning and evaluation of drinking-water supplies. The main purpose of this study is to investigate the accuracy of least square support vector machine (LSSVM), multivariate adaptive regression splines (MARS) and M5 Model Tree (M5Tree) in modeling Ep. The first part of the study focused on testing the ability of the LSSVM, MARS and M5Tree models in estimating the Ep data of Mersin and Antalya stations located in Mediterranean Region of Turkey by using cross-validation method. The LSSVM models outperformed the MARS and M5Tree models in estimating Ep of Mersin and Antalya stations with local input and output data. The average root mean square error (RMSE) of the M5Tree and MARS models was decreased by 24–32.1% and 10.8–18.9% using LSSVM models for the Mersin and Antalya stations, respectively. The ability of three different methods was examined in estimation of Ep using input air temperature, solar radiation, relative humidity and wind speed data from nearby station in the second part of the study (cross-station application without local input data). The results showed that the MARS models provided better accuracy than the LSSVM and M5Tree models with respect to RMSE, mean absolute error (MAE) and determination coefficient (R2) criteria. The average RMSE accuracy of the LSSVM and M5Tree was increased by 3.7% and 16.5% using MARS. In the case of without local input data, the average RMSE accuracy of the LSSVM and M5Tree was respectively increased by 11.4% and 18.4% using MARS. In the third part of the study, the ability of the applied models was examined in Ep estimation using input and output data of nearby station. The results reported that the MARS models performed better than the other models with respect to RMSE, MAE and R2 criteria. The average RMSE of the LSSVM and M5Tree was respectively decreased by 54% and 3.4% using MARS. The overall results indicated that the LSSVM could be successfully used in estimating Ep by using local input and output data while the MARS model performed better than the LSSVM in the case of without local input and outputs.
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•PLS-DA, PC-DFA, SVM and RF analyses were compared for metabolomics analyses.•Parsimonious models for feature selection and data reduction were presented.•Comparisons include ...generally recognized pros along with specific caveats for each of the methods.•Statistical models applied in the analysis of metabolomics data were shown.•Pros and cons of common analytical techniques used in metabolomics studies are highlighted.
The predominance of partial least squares-discriminant analysis (PLS-DA) used to analyze metabolomics datasets (indeed, it is the most well-known tool to perform classification and regression in metabolomics), can be said to have led to the point that not all researchers are fully aware of alternative multivariate classification algorithms. This may in part be due to the widespread availability of PLS-DA in most of the well-known statistical software packages, where its implementation is very easy if the default settings are used. In addition, one of the perceived advantages of PLS-DA is that it has the ability to analyze highly collinear and noisy data. Furthermore, the calibration model is known to provide a variety of useful statistics, such as prediction accuracy as well as scores and loadings plots. However, this method may provide misleading results, largely due to a lack of suitable statistical validation, when used by non-experts who are not aware of its potential limitations when used in conjunction with metabolomics. This tutorial review aims to provide an introductory overview to several straightforward statistical methods such as principal component-discriminant function analysis (PC-DFA), support vector machines (SVM) and random forests (RF), which could very easily be used either to augment PLS or as alternative supervised learning methods to PLS-DA. These methods can be said to be particularly appropriate for the analysis of large, highly-complex data sets which are common output(s) in metabolomics studies where the numbers of variables often far exceed the number of samples. In addition, these alternative techniques may be useful tools for generating parsimonious models through feature selection and data reduction, as well as providing more propitious results. We sincerely hope that the general reader is left with little doubt that there are several promising and readily available alternatives to PLS-DA, to analyze large and highly complex data sets.
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The electroencephalogram (EEG) signals are commonly used for diagnosis of epilepsy. In this paper, we present a new methodology for EEG-based automated diagnosis of epilepsy. Our method involves ...detection of key points at multiple scales in EEG signals using a pyramid of difference of Gaussian filtered signals. Local binary patterns (LBPs) are computed at these key points and the histogram of these patterns are considered as the feature set, which is fed to the support vector machine (SVM) for the classification of EEG signals. The proposed methodology has been investigated for the four well-known classification problems namely, 1) normal and epileptic seizure, 2) epileptic seizure and seizure free, 3) normal, epileptic seizure, and seizure free, and 4) epileptic seizure and nonseizure EEG signals using publically available university of Bonn EEG database. Our experimental results in terms of classification accuracies have been compared with existing methods for the classification of the aforementioned problems. Further, performance evaluation on another EEG dataset shows that our approach is effective for classification of seizure and seizure-free EEG signals. The proposed methodology based on the LBP computed at key points is simple and easy to implement for real-time epileptic seizure detection.
Forecasting the output power of solar systems is required for the good operation of the power grid or for the optimal management of the energy fluxes occurring into the solar system. Before ...forecasting the solar systems output, it is essential to focus the prediction on the solar irradiance. The global solar radiation forecasting can be performed by several methods; the two big categories are the cloud imagery combined with physical models, and the machine learning models. In this context, the objective of this paper is to give an overview of forecasting methods of solar irradiation using machine learning approaches. Although, a lot of papers describes methodologies like neural networks or support vector regression, it will be shown that other methods (regression tree, random forest, gradient boosting and many others) begin to be used in this context of prediction. The performance ranking of such methods is complicated due to the diversity of the data set, time step, forecasting horizon, set up and performance indicators. Overall, the error of prediction is quite equivalent. To improve the prediction performance some authors proposed the use of hybrid models or to use an ensemble forecast approach.
•Overview of forecasting methods of solar irradiation using machine learning approaches.•Performance ranking of such methods is complicated.•ANN and ARIMA methods are equivalent in term of quality of prediction.•Predictor ensemble methodology is always better than simple predictors.•SVM, regression trees and random forests, as the results given are very promising.
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•The structured variable selection in Kernel SVM is implemented using two ways.•The two-way interaction model is considered to enforce Heredity Principle.•SVMIC is used to select the kernel parameter ...in proposed approaches.•Simple and fast computations algorithms are derived.
Various applications of forecasting effective global horizontal irradiance play increasingly vital role in grid-connected photovoltaic installations, but suffer from forecasting inaccuracy and prohibitively expensive computational cost. Although Support Vector Machine (SVM) is one of the most powerful forecasting approaches, it does not provide an interpretable model. This motivates penalized variable selection methods to be introduced to SVM to select important variables. However, in some forecasting problems, there are some underlying logic or hierarchical structure such as heredity principle among the variables. Penalized Kernel SVM approaches do not take heredity principles into consideration when enforcing sparsity. This paper investigates structural variable selection in Kernel SVM based approach which pursues heredity principle and sparsity simultaneously. To achieve heredity principle, both optimization and procedure based structural variable selection approaches are studied in the Kernel SVM. Computationally, we derive fast and simple-to-implement algorithms to perform structural variable selection and solar irradiance forecasting. Furthermore, Support Vector Machines Information Criterion is utilized to select the kernel parameters to guarantee the model consistency. Real data experiments directly reveal that our proposed KSVM-SVS based approach following heredity principle delivers superior performances in terms of forecasting accuracy comparing with other competitors.
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Supervised machine learning algorithms have been a dominant method in the data mining field. Disease prediction using health data has recently shown a potential application area for these methods. ...This study ai7ms to identify the key trends among different types of supervised machine learning algorithms, and their performance and usage for disease risk prediction.
In this study, extensive research efforts were made to identify those studies that applied more than one supervised machine learning algorithm on single disease prediction. Two databases (i.e., Scopus and PubMed) were searched for different types of search items. Thus, we selected 48 articles in total for the comparison among variants supervised machine learning algorithms for disease prediction.
We found that the Support Vector Machine (SVM) algorithm is applied most frequently (in 29 studies) followed by the Naïve Bayes algorithm (in 23 studies). However, the Random Forest (RF) algorithm showed superior accuracy comparatively. Of the 17 studies where it was applied, RF showed the highest accuracy in 9 of them, i.e., 53%. This was followed by SVM which topped in 41% of the studies it was considered.
This study provides a wide overview of the relative performance of different variants of supervised machine learning algorithms for disease prediction. This important information of relative performance can be used to aid researchers in the selection of an appropriate supervised machine learning algorithm for their studies.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Over the past two decades, support vector machines (SVMs) have become a popular supervised machine learning model, and plenty of distinct algorithms are designed separately based on different KKT ...conditions of the SVM model for classification/regression with different losses, including convex and or nonconvex loss. In this paper, we propose an algorithm that can train different SVM models in a
unified
scheme. First, we introduce a definition of the least squares type of difference of convex loss (LS-DC) and show that the most commonly used losses in the SVM community are LS-DC loss or can be approximated by LS-DC loss. Based on the difference of convex algorithm (DCA), we then propose a unified algorithm called
UniSVM
which can solve the SVM model with any convex or nonconvex LS-DC loss, wherein only a vector is computed by the specifically chosen loss. UniSVM has a dominant advantage over all existing algorithms for training robust SVM models with nonconvex losses because it has a closed-form solution per iteration, while the existing algorithms always need to solve an L1SVM/L2SVM per iteration. Furthermore, by the low-rank approximation of the kernel matrix, UniSVM can solve large-scale nonlinear problems efficiently. To verify the efficacy and feasibility of the proposed algorithm, we perform many experiments on small artificial problems and large benchmark tasks both with and without outliers for classification and regression for comparison with state-of-the-art algorithms. The experimental results demonstrate that UniSVM can achieve comparable performance in less training time. The foremost advantage of UniSVM is that its core code in Matlab is less than 10 lines; hence, it can be easily grasped by users or researchers.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Safety is one of the key requirements for automated vehicles and fault diagnosis is an effective technique to enhance the vehicle safety. The model-based fault diagnosis method models the fault into ...the system model and estimates the faults by observer. In this article, to avoid the complexity of designing observer, we investigate the problem of steering actuator fault diagnosis for automated vehicles based on the approach of model-based support vector machine (SVM) classification. The system model is utilized to generate the residual signal as the training data and the data-based algorithm of the SVM classification is employed to diagnose the fault. Due to the phenomena of data unbalance induced poor performance of the data-driven method, an undersampling procedure with the approach of linear discriminant analysis and a threshold adjustment using the algorithm of grey wolf optimizer are proposed to modify and improve the performance of classification and fault diagnosis. Various comparisons are carried out based on widely used datasets. The comparison results show that the proposed algorithm has superiority on the classification over existing methods. Experimental results and comparisons of an automated vehicle illustrate the effectiveness of the proposed algorithm on the steering actuator fault diagnosis.
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