Nonlinear Principal Component Research Articles

The Modeling Method of a Vibrating Screen Efficiency Prediction Based on KPCA and LS-SVM

A vibrating screen efficiency prediction modeling method based on autoregressive (AR) model and least square support vector machine (LS-SVM) was proposed. The vibration signals of a self-synchronized vibrating screen were collected to establish the AR model. Nonlinear principal components of the signals were extracted by the kernel principal component analysis (KPCA), followed by the regression model reconstruction using LS-SVM to accomplish reduced complexity of the prediction model from AR coefficients and improved generalization capacity and learning speed. The results show that the model predictions are consistent with the experimental data, which indicates that the modeling method is applicable and feasible in adjusting the design and process parameters of vibrating screens. Furthermore, the work condition monitoring method in the experiment is feasible for faults diagnosis of mechanical equipment.

A Hybrid Model for Addressing the Relationship between Financial Performance and Sustainable Development

Measuring financial performance has become an essential topic due to the potential decimating impacts on the corporation itself as well as to whole societies during financial turmoil. In order to provide an overarching description of the multidimensional nature for measuring a corporation’s operations, it is preferable to employ data envelopment analysis (DEA). Different from prior research that merely focuses on a singular DEA performance rank, this study extends it to multiple DEA specifications (i.e., it combines inputs and outputs in several different ways) so as to make judgments more complete and robust. We also execute fuzzy visualization technique (i.e., nonlinear fuzzy robust principal component analysis, NFRPCA) to represent the main characteristics of data so that non-specialists can have better access to the results. The analyzed result is then fed into the restricted Boltzmann machine (RBM) to establish a model to forecast a firm’s operating performance. Even a fraction of accuracy improvement can result in considerable future savings to a firm and investors. When examined using real cases, the model is a promising alternative for operating performance forecasting and can assist both internal and external market participants.

Parallel neural networks for improved nonlinear principal component analysis

In this paper, a parallel neural network architecture is proposed to improve the performance of neural-network-based nonlinear principal component analysis. There exist two typical approaches for such analysis: simultaneous extraction of principal components using a single autoassociative neural network (also known as autoencoder), and sequential extraction using multiple neural networks in series. The proposed architecture can be obtained by systematically pruning the network connections of a fully connected autoassociative neural network, resulting in decoupled neural networks. As a result, more independent (i.e., less correlated) principal components can be obtained than the simultaneous extraction approach. The proposed architecture can be also viewed as a rearrangement of multiple neural networks for the sequential extraction in a parallel setting, and thus, the network training becomes more efficient. Simulation case studies are performed to illustrate the advantages of the proposed architecture, and it was shown that it is particularly beneficial for deep neural networks.

Decomposition of passive smoking inequality in Iranian children and adolescents: the CASPIAN-V Study

To investigate the socioeconomic inequality in passive smoking in Iranian children and adolescents. Through a multistage random cluster sampling method, a representative sample of 14,400 school students, aged 6-18 years, were enrolled from urban and rural areas of 30 provinces of Iran. Using a non-linear principal component analysis (NLPCA), the correlated variables were summarized as socioeconomic status (SES). Normalized concentration index (NCI) was used to measure inequality in passive smoking at national and regional SES levels. We decomposed total socioeconomic inequality in passive smoking into explanatory variables to identify the main contributors of inequality in passive smoking in the population studied. Data of 12,327 students and parents were complete for the current study. The response rate was 85.6% (50.9% boys, 71.2% urban residents). About 44% of Iranian students were exposed to passive smoking. The NCI for passive smoking at a national level was - 0.043 (95% confidence interval - 0.035, - 0.012), indicating that passive smoking was more concentrated among poorer children and adolescents. This inequality was statistically significant at a national level and in all regions except for regions with lower middle- and lowest SES levels. Considering the decomposition analysis, the household SES (63%), mothers' educational level (37%), fathers' educational level (29%), and school type (18%) made the largest positive contribution to inequality in passive smoking of children and adolescents. Passive smoking was distributed unequally among Iranian children and adolescents; it was more concentrated among socioeconomically disadvantaged families. Public health policies attentions should be given to reduce passive smoking among low SES children and adolescents living with illiterate or low-educated parents.

A Method for Identifying Most Significant Vehicle Parameters for Controller Performance of Autonomous Driving Functions

<div class="section abstract"><div class="htmlview paragraph">In this paper a method for the identification of most significant vehicle parameters influencing the behavior of a lateral control system of autonomous car is presented. Requirements for the design stage of the controller need to consider many uncertainties in the plant. While most vehicle properties can be compensated by an appropriate tuning of the control parameters, other vehicle properties can change significantly during usage. The control system is evaluated based on performance measures. Analyzed parameters comprise functional tire characteristics, mass of the vehicle and position of its center of gravity. Since the parameters are correlated, but Sobol’ sensitivity analysis assumes decorrelated inputs, random variation yields no reasonable results. Furthermore, the variation of each parameter or set of parameters is not applicable since the numbers of required simulations is increased significantly according to input dimension. Therefore, the proposed methodology determines directly the influence of the parameters on performance measures of the lateral control function. First, a hierarchical nonlinear principal component analysis using artificial neural networks is used to decouple inputs and reduce them to a lower dimensional subspace. In a second step Sobol’ sensitivity analysis is used to identify the significant parameters by sampling in the decoupled subspace and their evaluation by a full-blown vehicle dynamics simulation. The most significant parameters are merged in a static and dynamic variable resulting in four worst-case vehicle states. Applying the methodology of the Cooperative Design Approach, performance measures of the lateral control function (rising time, overshoot, number of oscillations) can be analyzed for each vehicle state yielding a robust control design.</div></div>

Farm-level risk factors for bovine mastitis in Dutch automatic milking dairy herds

Automatic milking systems (AMS) are installed on a growing number of dairy farms worldwide. Management to support good udder health might be different on farms with an AMS compared with farms milking with a conventional milking system, as risk factors for mastitis on farms using an AMS may differ. The aim of this study was to identify farm level factors associated with mastitis on Dutch dairy farms using an AMS. In 2008, risk factor data were collected using a questionnaire combined with on-farm recordings of cow, stall, and AMS hygiene on 135 farms. These risk factor data were linked to 4 udder-health-associated dependent variables: average herd somatic cell count (HeSCCav), variance of the average herd somatic cell count (SCC) on test days (HeSCCvar), the average proportion of new high SCC cases (NHiSCC), and the farmer-reported annual incidence rate of clinical mastitis (IRCM). We employed regression models using multiple imputation to deal with missing values. Due to the high dimensionality of the risk factor data, we also performed nonlinear principal component analysis (NLPCA) and regressed the dependent variables on the principal components (PC). Good hygiene of cows and of AMS were found to be related to a lower HeSCCav and less NHiSCC. Effective postmilking teat disinfection was associated with a lower NHiSCC. A higher bulk tank milk SCC threshold for farmers' action was related to more NHiSCC. Larger farm size was related to lower HeSCCvar but higher NHiSCC. Negative attitude of farmers to animal health, higher frequency of checking AMS, and more time spent on viewing computer data were all positively related to higher IRCM. An NLPCA with 3 PC explained 16.3% of the variance in the risk factor variables. Only the first 2 PC were associated with mastitis. The first PC reflected older and larger farms with poor cow hygiene and AMS hygiene, and was related to higher HeSCCav and NHiSCC, whereas the second PC reflected newly built smaller farms with poor cow hygiene and low milk production, and was associated with higher HeSCCvar and NHiSCC, but lower IRCM. Our study suggests that many of the risk factors on conventional milking system farms are applicable to AMS farms, specifically concerning hygiene of the cows and the milking machine, but on large AMS farms, udder health may need more attention than on smaller AMS farms. Multiple imputation is instrumental to deal with missing values and NLPCA is a useful technique to process high dimensional data in our study.

Multi-Scenario Species Distribution Modeling.

Correlative species distribution models (SDMs) are increasingly being used to predict suitable insect habitats. There is also much criticism of prediction discrepancies among different SDMs for the same species and the lack of effective communication about SDM prediction uncertainty. In this paper, we undertook a factorial study to investigate the effects of various modeling components (species-training-datasets, predictor variables, dimension-reduction methods, and model types) on the accuracy of SDM predictions, with the aim of identifying sources of discrepancy and uncertainty. We found that model type was the major factor causing variation in species-distribution predictions among the various modeling components tested. We also found that different combinations of modeling components could significantly increase or decrease the performance of a model. This result indicated the importance of keeping modeling components constant for comparing a given SDM result. With all modeling components, constant, machine-learning models seem to outperform other model types. We also found that, on average, the Hierarchical Non-Linear Principal Components Analysis dimension-reduction method improved model performance more than other methods tested. We also found that the widely used confusion-matrix-based model-performance indices such as the area under the receiving operating characteristic curve (AUC), sensitivity, and Kappa do not necessarily help select the best model from a set of models if variation in performance is not large. To conclude, model result discrepancies do not necessarily suggest lack of robustness in correlative modeling as they can also occur due to inappropriate selection of modeling components. In addition, more research on model performance evaluation is required for developing robust and sensitive model evaluation methods. Undertaking multi-scenario species-distribution modeling, where possible, is likely to mitigate errors arising from inappropriate modeling components selection, and provide end users with better information on the resulting model prediction uncertainty.

A Damage Classification Approach for Structural Health Monitoring Using Machine Learning

Inspection strategies with guided wave-based approaches give to structural health monitoring (SHM) applications several advantages, among them, the possibility of the use of real data from the structure which enables continuous monitoring and online damage identification. These kinds of inspection strategies are based on the fact that these waves can propagate over relatively long distances and are able to interact sensitively with and uniquely with different types of defects. The principal goal for SHM is oriented to the development of efficient methodologies to process these data and provide results associated with the different levels of the damage identification process. As a contribution, this work presents a damage detection and classification methodology which includes the use of data collected from a structure under different structural states by means of a piezoelectric sensor network taking advantage of the use of guided waves, hierarchical nonlinear principal component analysis (h-NLPCA), and machine learning. The methodology is evaluated and tested in two structures: (i) a carbon fibre reinforced polymer (CFRP) sandwich structure with some damages on the multilayered composite sandwich structure and (ii) a CFRP composite plate. Damages in the structures were intentionally produced to simulate different damage mechanisms, that is, delamination and cracking of the skin.

Profiling of Oncogenic Signaling in Multiple Myeloma — Association with Biology, Disease Progression and Prognosis

Introduction: The pathogenesis of multiple myeloma (MM) involves complex genetic and epigenetic alterations, which ultimately affect oncogenic signaling networks. Analysis of pathway activation in primary MM cells within their native microenvironment will not only increase our understanding of MM cell biology but may open up new avenues towards tailored therapies that target individually activated signaling cascades.Methods: We established a (phospho-) immunohistochemistry (IHC)-assay to analyze key activation markers of oncogenic pathways and networks, namely RAS/RAF/MEK/ERK, PI3K/AKT, JAK/STAT3, canonical NFκB, and cMyc, in an exploratory cohort of 459 bone marrow (BM) biopsies from patients with smoldering MM (SMM), newly diagnosed symptomatic (NDMM) and refractory MM (RRMM). An independent validation cohort comprised BM samples from 462 NDMM patients who were uniformly treated within clinical trials. To integrate activation intensity and spread scores for each pathway and sample, non-linear principal component analysis (PCA) was applied.Results: Pathway profiling in progressing stages of plasma cell disorders showed the canonical NFκB pathway as nearly universally activated in SMM (97% of patients); signals for AKT, ERK, STAT3, and cMyc were only seen in subsets of SMM patients (≤33%), with STAT3 being the least activated pathway (6%). In NDMM and RRMM, NFκB activation significantly decreased to 90% and 82% of patients, respectively (p=0.02). In contrast, we observed a significant increase in activation of all other pathways and cMyc from SMM to NDMM and further to RRMM patients (p<0.001). All of these pathways were activated in at least 50% of RRMM. Analysis of 34 patients with available longitudinal samples confirmed a significant increase in strong activation of STAT3 and cMyc with progressing disease stages.Assessment of pathway clustering and interdependencies showed a strong negative correlation between NFκB and ERK in all disease stages. In contrast, a positive correlation was seen between cMyc and STAT3 as well as between AKT and ERK activation with increasing strength over progressing disease stages. Interestingly, when focusing on strong activation signals as determined by PCA, virtual absence of simultaneous activation of more than one pathway in SMM was observed, while pathway co-activation significantly increased in NDMM and RRMM (22% and 34%, p<0.001).Activation of STAT3 and expression of cMyc were significantly associated with higher ISS stage in NDMM of both the exploratory and the validation cohort. STAT3 pathway activation was also significantly associated with gain(1q21). In the validation cohort with available gene expression profiling (GEP) data, both STAT3 (p=0.02) and cMyc (p<0.001) activation showed a significant association with high risk disease according to GEP risk scores.Assessment of the impact of pathway activation on survival in the validation cohort showed expression of cMyc (p=0.01), activation of STAT3 (p=0.005) and cMyc-STAT3 co-activation (p=0.005) to negatively influence PFS. Expression of cMyc (p=0.02) and cMyc-STAT3 co-activation (p=0.02) were also significantly associated with inferior OS. STAT3 activation showed a negative trend towards inferior OS (p=0.08). Accounting for GEP-defined risk status, STAT3 pathway activation defined an ultra-high risk cohort. Assessment of differential gene expression suggested an autocrine positive feedback loop of IL-6 signaling and an enrichment of cell cycle associated genes in patients with STAT3 activation.Conclusions: Activation of either NFkB or ERK/AKT signaling appears to be a hallmark of MM. Later stages of MM are characterized by activation of additional signaling pathways that may affect therapy response. Importantly, cMyc expression and JAK/STAT3 pathway activation show adverse prognostic impact and significantly increase with disease progression. DisclosuresGoldschmidt:Sanofi: Consultancy, Research Funding; Adaptive Biotechnology: Consultancy; Amgen: Consultancy, Research Funding; Janssen: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Bristol Myers Squibb: Consultancy, Honoraria, Research Funding; ArtTempi: Honoraria; Novartis: Honoraria, Research Funding; Mundipharma: Research Funding; Chugai: Honoraria, Research Funding; Takeda: Consultancy, Research Funding. Raab:BMS: Consultancy, Honoraria, Research Funding; Amgen: Consultancy, Honoraria, Research Funding; Novartis: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria.

Gas path fault diagnostics using a hybrid intelligent method for industrial gas turbine engines

There are many challenges against an accurate gas turbine fault diagnostics, such as the nonlinearity of the engine health, the measurement uncertainty, and the occurrence of simultaneous faults. The conventional methods have limitations in effectively handling these challenges. In this paper, a hybrid intelligent technique is devised by integrating an autoassociative neural network (AANN), nested machine learning (ML) classifiers, and a multilayer perceptron (MLP). The AANN module is used as a data preprocessor to reduce measurement noise and extract the important features for visualisation and fault diagnostics. The features are extracted from the bottleneck layer output values based on the concept of the nonlinear principal component analysis (NLPCA). The nested classifier modules are then used in such a manner that fault and no-fault conditions, component and sensor faults, and different component faults are distinguished hierarchically. As part of the classification, evaluation of the fault classification performance of five widely used ML techniques aiming to identify alternative approaches is undertaken. In the end, the MLP approximator is utilised to estimate the magnitude of the isolated component faults in terms of flow capacity and isentropic efficiency indices. The developed system was implemented to diagnose up to three simultaneous faults in a two-shaft industrial gas turbine engine. Its robustness towards the measurement uncertainty was also evaluated based on Gaussian noise corrupted data. The test results show the derivable benefits of integrating two or more methods in engine diagnostics on the basis of offsetting the weakness of the one with the strength of another.

Analiza nieliniowych składowych głównych dla danych czasowo‑przestrzennych geograficznie ważonych

Schölkopf, Smola i Müller (1998) zaproponowali analizę nieliniowych składowych głównych (NPCA) dla ustalonych danych wektorowych. Niniejszy artykuł zawiera rozszerzenie tej metody na dane czasowo‑przestrzenne oraz czasowo‑przestrzenne geograficznie ważone. Każdy obiekt jest scharakteryzowany za pomocą macierzy Xi, rozmiaru T × p, zawierającej wartości p cech zaobserwowanych w T momentach czasowych, i = 1, …, n. Macierze te są przekształcane nieliniowo do przestrzeni Hilberta i budowana jest scentrowana macierz jądrowa. Ostatecznie macierz ta jest podstawą konstrukcji nieliniowych składowych głównych. W przypadku danych geograficznie ważonych macierz Xizostaje zastąpiona macierzą wiXi, gdzie wijest dodatnią wagą geograficzną związaną z i‑tym miejscem obserwacji, i = 1, …, n. Teoria zilustrowana jest przykładem dotyczącym stanu szkolnictwa wyższego w 16 polskich województwach, notowanego w latach 2002–2016.

Deep principal component analysis: An enhanced approach for structural damage identification

The structural health monitoring relies on the continuous observation of a dynamic system over time to identify its actual condition, detect abnormal behaviors, and predict future states. The regular changes in environmental factors have been reported as one of the main challenges for the application of structural health monitoring systems. These influences in the structural responses are in general nonlinear, affecting the damage-sensitive features in the most varied forms. The usual process to remove these normal changes is referred to as data normalization. In that regard, principal component analysis is probably the most studied algorithm in structural health monitoring, having numerous versions to learn strong nonlinear normal changes. However, in most cases, not all variability is properly accounted for via the existing nonlinear principal component analysis approaches, resulting in poor damage detection and quantification performances. In this article, a new paradigm based on deep principal component analysis, rooted in the deep learning field, is presented to overcome these limitations. This approach extracts the most salient underlying feature distributions by stacking multiple feedforward neural networks trained to learn an identity mapping of the input variables, where the network inputs are reproduced into the outputs. Similar to the traditional nonlinear principal component analysis–based approach, our approach identifies a nonlinear output-only model of an undamaged structure by comprising modal features into an internal bottleneck layer, which implicitly represents the independent environmental factors. The proposed technique is validated through the application on a progressively damaged prestressed concrete bridge and a three-span suspension bridge. The experimental results demonstrate that capturing the most slight nonlinear variations in the data can lead to improved data normalization and, consequently, better damage detection and quantification performances.

Research on Recognition Method of Driving Fatigue State Based on Sample Entropy and Kernel Principal Component Analysis

In view of the nonlinear characteristics of electroencephalography (EEG) signals collected in the driving fatigue state recognition research and the issue that the recognition accuracy of the driving fatigue state recognition method based on EEG is still unsatisfactory, this paper proposes a driving fatigue recognition method based on sample entropy (SE) and kernel principal component analysis (KPCA), which combines the advantage of the high recognition accuracy of sample entropy and the advantages of KPCA in dimensionality reduction for nonlinear principal components and the strong non-linear processing capability. By using support vector machine (SVM) classifier, the proposed method (called SE_KPCA) is tested on the EEG data, and compared with those based on fuzzy entropy (FE), combination entropy (CE), three kinds of entropies including SE, FE and CE that merged with KPCA. Experiment results show that the method is effective.

Linear and nonlinear features and machine learning for wind turbine blade ice detection and diagnosis

Abstract The mass of ice on wind turbines blades is one of the main problems that energy companies have in cold climates. This paper presents a novel approach to detect and classify ice thickness based on pattern recognition through guided ultrasonic waves and Machine Learning. To successfully achieve a supervised classification, it is necessary to employ a method that allows the correct extraction and selection of features of the ultrasonic signal. The main novelty in this work is that the approach considers four feature extraction methods to validate the results, grouped by linear (AutoRegressive (AR) and Principal Component Analysis) and nonlinear (nonlinear-AR eXogenous and Hierarchical Non-Linear Principal Component Analysis), and feature selection is done by Neighbourhood Component Analysis. A supervised classification was performed through Machine Learning with twenty classifiers such as Decision tree, Discriminant Analysis, Support Vector Machines, K-Nearest Neighbours, and Ensemble Classifiers. Finally, an evaluation of the classifiers was done in single frequency and multi-frequency modes, obtaining accurate results.

An Improved Diagnostic Tool for Control Valve Stiction Based on Nonlinear Principle Component Analysis

Control valves suffer from wear and aging as the valves open and close. Such continuous movements result in many operational issues, and one of the widely known problems is stiction nonlinearity. Stiction results in inferior quality of the products, large rejection rates, increased energy consumption, and reduced profitability. In this paper, a simple algorithm that combines preprocessing and postprocessing as well as average crossing autocovariance (AC) together with the nonlinear principal component analysis (NLPCA) is investigated. The results obtained from the simulated and industrial case studies show that the proposed NLPCA-AC method has favorable proficiencies for control valve stiction detection.

Relación entre gobierno corporativo y desempeño financiero: evidencia para las empresas colombianas durante el periodo de 2006 a 2013

Los atributos de gobierno corporativo de una empresa son de gran importancia para sus inversionistas, debido a la relación que estos guardan con el desempeño financiero. Este trabajo busca soportar la evidencia internacional sobre tal relación y justifica la implementación de buenas prácticas de gobierno corporativo en las empresas colombianas que cotizan en el mercado de valores. Para ello, se implementa conjuntamente un análisis no lineal de componentes principales y el método generalizado de momentos. En este ejercicio, se extraen las complejas dimensiones de gobierno corporativo a través de sus relaciones locales y, luego, se evalúa su relación con los indicadores de desempeño financiero: ROA y Q de Tobin.

Analysis of factors affecting outcomes of pregnancy complicated by Echinococcus: an algorithm for approach and management.

The management of hydatid disease (HD) co-occurring with pregnancy remains a challenge for physicians. We aimed to determine factors that were related to fetal and maternal outcomes in HD complicated pregnancies and then develop an approach/treatment algorithm. All patients at the participating hospitals were first analyzed to determine whether they had HD. Only patients diagnosed with HD during the course of their pregnancy were included. Certain cyst-related factors (diameter, localization, increase in size, and viability) and certain pregnancy-related factors (treatments, gestational week, maternal co-morbidities, and delivery type) were investigated. Nonlinear principal component analysis (NPCA) was performed to determine the relationships between the categories of variables. Out of 12,926 pregnancies, 27 cases were diagnosed with HD. In 13 cases, each developed at least one fetal problem. Using Albendazol in first trimester, presence of an active cyst, increased diameter by more than 1cm during pregnancy and a cyst diagnosed in the second trimester were associated with at least one fetal problem. According to the NPCA results, cyst diameter when first diagnosed was related to fetal outcomes; a cyst greater than 10cm was associated with "at least one fetal problem". Cysts 5-10cm in diameter were in a neutral position, while 2-5cm in diameter were in the "no problems" group. HD mostly affects fetus. If cyst-related and pregnancy-related variables are optimal, close follow-up on a monthly is the best course of action. However, in high-risk conditions, percutaneous interventions or surgery should be considered.

Improving the Statistical Capacity Index: A Statistical Approach

Good quality, timely and accurate statistics lie at the heart of a country's effort to improve development effectiveness. As a response to the challenge of measuring the institutional capacity of a country in producing timely and accurate statistics, the World Bank developed its framework for the Statistical Capacity Index (SCI). Although the World Bank's framework is acknowledged for its simplistic approach, it has received extensive critique for the ad-hoc allocation of weights. This research attempts to find a solution to this criticism using a statistical methodology. Country information used by the World Bank to create the SCI for the year 2014 was considered. The data consisted information on 25 categorical variables out of which 16 were binary variables and 9 were ordinal variables. Nonlinear Principal Component Analysis (NLPCA) was conducted on the categorical data to reduce the observed variables to uncorrelated principal components. Consequently, the optimally scaled variables were used as input for factor analysis with principal component extraction. The results of the factor analysis were used to weight the new SCI. The dimension, availability and periodicity of economic and financial indicators explained most of the variance in the data set. The research proposes a simpler version of the new SCI with only 23 variables. In the proposed new index, the variables enrolment reporting to UNESCO, gender equality in education and primary completion indicators were the three variables receiving the largest weight. These three indicators measure the periodicity of reporting data on educational statistics to UNESCO; periodicity of observing the gross enrolment rate of girls to boys in primary and secondary education; and periodicity of observing the PCR indicator which is the number of children reaching the last year of primary school net of repeaters respectively. This research represents the first attempt to create a SCI using multivariate statistical techniques and especially index construction with NLPCA. The research concluded with a comparison of the proposed new index and the index created by the World Bank, which justified that the proposed index be used as a solution for the arbitrary allocation of weights in creating the SCI.

Improving corporate bond recovery rate prediction using multi-factor support vector regressions

In the multi-factor framework described in this paper, we use instrument-specific characteristics, several macroeconomic variables, and industry-specific characteristics as our explanatory variables for predicting recovery rates for corporate bonds. By including the principal components derived from a large number of macroeconomic variables, all three least-squares support vector regression methods, as well as the ordinary linear regression, exhibit higher out-of-sample predictive accuracy than the models that included only the few macroeconomic variables suggested in the literature. We compare the prediction accuracies of all techniques by incorporating sparse principal components, nonlinear principal components from an auto-associative neural network, and kernel principal components. Our results show that sparse principal components generate more interpretable and accurate estimations compared to the other principal component techniques. Moreover, we apply gradient boosting to generate a ranking of the 104 macroeconomic variables, from best to worst, based on their prediction power in recovery rate estimation. The three categories with the most informative macroeconomic predictors are micro-level factors, business cycle variables, and stock market indicators.

Multi-objective ensemble forecasting with an application to power transformers

In this paper we present an ensemble time series forecasting algorithm using evolutionary multi-objective optimization algorithms to predict dissolved gas contents in power transformers. In this method, the correlation between each individual dissolved gas and other transformers’ features such as temperature characteristics and loading history is first determined. Then, a non-linear principal component analysis (NLPCA) technique is applied to extract the most effective time series from the highly correlated features. Afterwards, the forecasting algorithms are trained using a cross validation technique. In addition, evolutionary multi-objective optimization algorithms are used to select the most accurate and diverse group of forecasting algorithms to construct an ensemble. Finally, the selected ensemble is examined to predict the value of the dissolved gases on the testing set. The results of one day, two day, three day, and four day ahead forecasting are presented which show higher accuracy and reliability of the proposed method compared with other statistical methods.