Modeling, forecasting and trading the EUR exchange rates with hybrid rolling genetic algorithms: support vector regression forecast combinations

The motivation of this paper is to introduce a hybrid Rolling Genetic Algorithm-Support Vector Regression (RG-SVR) model for optimal parameter selection and feature subset combination. The algorithm is applied to the task of forecasting and trading the EUR/USD, EUR/GBP and EUR/JPY exchange rates. The proposed methodology genetically searches over a feature space (pool of individual forecasts) and then combines the optimal feature subsets (SVR forecast combinations) for each exchange rate. This is achieved by applying a fitness function specialized for financial purposes and adopting a sliding window approach. The individual forecasts are derived from several linear and non-linear models. RG-SVR is benchmarked against genetically and non-genetically optimized SVRs and SVMs models that are dominating the relevant literature, along with the robust ARBF-PSO neural network. The statistical and trading performance of all models is investigated during the period of 1999–2012. As it turns out, RG-SVR presents the best performance in terms of statistical accuracy and trading efficiency for all the exchange rates under study. This superiority confirms the success of the implemented fitness function and training procedure, while it validates the benefits of the proposed algorithm

European exchange trading funds trading with locally weighted support vector regression

In this paper, two different Locally Weighted Support Vector Regression (wSVR) algorithms are generated and applied to the task of forecasting and trading five European Exchange Traded Funds. The trading application covers the recent European Monetary Union debt crisis. The performance of the proposed models is benchmarked against traditional Support Vector Regression (SVR) models. The Radial Basis Function, the Wavelet and the Mahalanobis kernel are explored and tested as SVR kernels. Finally, a novel statistical SVR input selection procedure is introduced based on a principal component analysis and the Hansen, Lunde, and Nason (2011) model confidence test. The results demonstrate the superiority of the wSVR models over the traditional SVRs and of the v-SVR over the ε-SVR algorithms. We note that the performance of all models varies and considerably deteriorates in the peak of the debt crisis. In terms of the kernels, our results do not confirm the belief that the Radial Basis Function is the optimum choice for financial series

Improved RBF Network Intrusion Detection Model Based on Edge Computing with Multi-algorithm Fusion

Edge computing is difficult to deploy a complete and reliable security strategy due to its distributed computing architecture and inherent heterogeneity of equipment and limited resources. When malicious attacks occur, the loss will be immeasurable. RBF neural network has strong nonlinear representation ability and fast learning convergence speed, which is suitable for intrusion detection of edge detection industrial control network. In this paper, an improved RBF network intrusion detection model based on multi-algorithm fusion is proposed. kernel principal component analysis (KPCA) is used to extract data dimension and simplify data representation. Then subtractive clustering algorithm(SCM) and grey wolf algorithm(GWO) are used to jointly optimize RBF neural network parameters to avoid falling into local optimum, reduce the calculation of model training and improve the detection accuracy. The algorithm can better adapt to the edge computing platform with weak computing ability and bearing capacity, and realize real-time data analysis.The experimental results of BATADAL data set and Gas data set show that the accuracy of the algorithm is over 99% and the training time of larger samples is shortened by 50 times for BATADAL data set. The results show that the improved RBF network is effective in improving the convergence speed and accuracy in intrusion detection

On the design of an ECOC-compliant genetic algorithm

Genetic Algorithms (GA) have been previously applied to Error-Correcting Output Codes (ECOC) in state-of-the-art works in order to find a suitable coding matrix. Nevertheless, none of the presented techniques directly take into account the properties of the ECOC matrix. As a result the considered search space is unnecessarily large. In this paper, a novel Genetic strategy to optimize the ECOC coding step is presented. This novel strategy redefines the usual crossover and mutation operators in order to take into account the theoretical properties of the ECOC framework. Thus, it reduces the search space and lets the algorithm to converge faster. In addition, a novel operator that is able to enlarge the code in a smart way is introduced. The novel methodology is tested on several UCI datasets and four challenging computer vision problems. Furthermore, the analysis of the results done in terms of performance, code length and number of Support Vectors shows that the optimization process is able to find very efficient codes, in terms of the trade-off between classification performance and the number of classifiers. Finally, classification performance per dichotomizer results shows that the novel proposal is able to obtain similar or even better results while defining a more compact number of dichotomies and SVs compared to state-of-the-art approaches

Compositional optimization of hard-magnetic phases with machine-learning models

Machine Learning (ML) plays an increasingly important role in the discovery and design of new materials. In this paper, we demonstrate the potential of ML for materials research using hard-magnetic phases as an illustrative case. We build kernel-based ML models to predict optimal chemical compositions for new permanent magnets, which are key components in many green-energy technologies. The magnetic-property data used for training and testing the ML models are obtained from a combinatorial high-throughput screening based on density-functional theory calculations. Our straightforward choice of describing the different configurations enables the subsequent use of the ML models for compositional optimization and thereby the prediction of promising substitutes of state-of-the-art magnetic materials like Nd$_2$Fe$_{14}$B with similar intrinsic hard-magnetic properties but a lower amount of critical rare-earth elements.Comment: 12 pages, 6 figure

A Hybrid Clustering-Fusion Methodology for Land Subsidence Estimation

A hybrid clustering-fusion methodology is developed in this study that employs Genetic Algorithm (GA) optimization method, k-means method, and several soft computing (SC) models to better estimate land subsidence. Estimation of land subsidence is important in planning and management of groundwater resources to prevent associated catastrophic damages. Methods such as the Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) can be used to estimate the subsidence rate, but PS-InSAR does not offer the required efficiency and accuracy in noisy pixels (obtained from remote sensing). Alternatively, a fusion-based methodology can be used to estimate subsidence rate, which offers a superior accuracy as opposed to the traditionally used methods. In the proposed methodology, five SC methods are employed with hydrogeological forcing of frequency and thickness of fine-grained sediments, groundwater depth, water level decline, transmissivity and storage coefficient, and output of land subsidence rate. Results of individual SC models are then fused to render more accurate land subsidence rate in noisy pixels, for which PS-InSAR cannot be effective. We first extract 14,392 different input-output patterns from PS-InSAR technique for our study area in Tehran province, Iran. Then, k-means method is used to divide the study area to homogenous zones with similar features. The five SC models include Adaptive Neuro Fuzzy Inference System (ANFIS), Support Vector Regression (SVR), Multi-Layer Perceptron (MLP) neural network and two optimized models, namely, Radial Basis Function (RBF) and Generalized Regression Neural Network (GRNN). To fuse individual SC models, three methods including Genetic Algorithm (GA), K-Nearest Neighbors (KNN) and Ordered Weighted Average (OWA) based on ORNESS method and ORLIKE method, are developed and evaluated. Results show that the fusion-based method is significantly superior to each of the employed individual methods in predicting land subsidence rate