A Comparative Study of Support Vector Machine and Artificial Neural Network for Option Price Prediction

Option pricing has become one of the quite important parts of the financial market. As the market is always dynamic, it is really difficult to predict the option price accurately. For this reason, various machine learning techniques have been designed and developed to deal with the problem of predicting the future trend of option price. In this paper, we compare the effectiveness of Support Vector Machine (SVM) and Artificial Neural Network (ANN) models for the prediction of option price. Both models are tested with a benchmark publicly available dataset namely SPY option price-2015 in both testing and training phases. The converted data through Principal Component Analysis (PCA) is used in both models to achieve better prediction accuracy. On the other hand, the entire dataset is partitioned into two groups of training (70%) and test sets (30%) to avoid overfitting problem. The outcomes of the SVM model are compared with those of the ANN model based on the root mean square errors (RMSE). It is demonstrated by the experimental results that the ANN model performs better than the SVM model, and the predicted option prices are in good agreement with the corresponding actual option prices.

Possible contribution of artificial neural networks and linear discriminant analysis in recognition of patients with suspected atrophic body gastritis

AIM: To investigate whether ANNs and LDA could recognize patients with ABG in a database, containing only clinical and biochemical variables, of a pool of patients with and without ABG, by selecting the most predictive variables and by reducing input data to the minimum.METHODS: Data was collected from 350 consecutive outpatients (263 with ABG, 87 with non-atrophic gastritis and/or celiac disease [controls]). Structured questionnaires with 22 items (anagraphic, anamnestic, clinical, and biochemical data) were filled out for each patient. All patients underwent gastroscopy with biopsies. ANNs and LDA were applied to recognize patients with ABG.Experiment 1: random selection on 37 variables, experiment 2: optimization process on 30 variables, experiment 3:input data reduction on 8 variables, experiment 4: use of only clinical input data on 5 variables, and experiment 5:use of only serological variables.RESULTS: In experiment 1, overall accuracies of ANNs and LDA were 96.6% and 94.6%, respectively, for predicting patients with ABG. In experiment 2, ANNs and LDA reached an overall accuracy of 98.8% and 96.8%,respectively. In experiment 3, overall accuracy of ANNs was 98.4%. In experiment 4, overall accuracies of ANNs and LDA were, respectively, 91.3% and 88.6%. In experiment 5, overall accuracies of ANNs and LDA were,respectively, 97.7% and 94.5%.CONCLUSION: This preliminary study suggests that advanced statistical methods, not only ANNs, but also LDA,may contribute to better address bioptic sampling during gastroscopy in a subset of patients in whom ABG may be suspected on the basis of aspecific gastrointestinal symptoms or non-digestive disorders.

Machine Learning and Artificial Neural Network for Predicting Heart Failure Risk

Heart failure is now widely spread throughout the world.Heart disease affects approximately 48%of the population.It is too expensive and also difficult to cure the disease.This research paper represents machine learning models to predict heart failure.The fundamental concept is to compare the correctness of various Machine Learning(ML)algorithms and boost algorithms to improve models’accuracy for prediction.Some supervised algorithms like K-Nearest Neighbor(KNN),Support Vector Machine(SVM),Decision Trees(DT),Random Forest(RF),Logistic Regression(LR)are considered to achieve the best results.Some boosting algorithms like Extreme Gradient Boosting(XGBoost)and Cat-Boost are also used to improve the prediction using Artificial Neural Networks(ANN).This research also focuses on data visualization to identify patterns,trends,and outliers in a massive data set.Python and Scikit-learns are used for ML.Tensor Flow and Keras,along with Python,are used for ANN model train-ing.The DT and RF algorithms achieved the highest accuracy of 95%among the classifiers.Meanwhile,KNN obtained a second height accuracy of 93.33%.XGBoost had a gratified accuracy of 91.67%,SVM,CATBoost,and ANN had an accuracy of 90%,and LR had 88.33%accuracy.

Intelligent Decision Support System for Bank Loans Risk Classification

Intelligent Decision Support System (IISS) for Bank Loans Risk Classification (BLRC), based on the way of integration Artificial Neural Network (ANN) and Expert System (ES), is proposed. According to the feature of BLRC, the key financial and non-financial factors are analyzed. Meanwhile, ES and Model Base (MB) which contain ANN are designed . The general framework,interaction and integration of the system are given. In addition, how the system realizes BLRC is elucidated in detail.

Artificial Intelligence Based Meteorological Parameter Forecasting for Optimizing Response of Nuclear Emergency Decision Support System

This paper presents a novel artificial intelligence (AI) based approach to predict crucial meteorological parameters such as temperature,pressure,and wind speed,typically calculated from computationally intensive weather research and forecasting (WRF) model.Accurate meteorological data is indispensable for simulating the release of radioactive effluents,especially in dispersion modeling for nuclear emergency decision support systems.Simulation of meteorological conditions during nuclear emergencies using the conventional WRF model is very complex and time-consuming.Therefore,a new artificial neural network (ANN) based technique was proposed as a viable alternative for meteorological prediction.A multi-input multi-output neural network was trained using historical site-specific meteorological data to forecast the meteorological parameters.Comprehensive evaluation of this technique was conducted to test its performance in forecasting various parameters including atmospheric pressure,temperature,and wind speed components in both East-West and North-South directions.The performance of developed network was evaluated on an unknown dataset,and acquired results are within the acceptable range for all meteorological parameters.Results show that ANNs possess the capability to forecast meteorological parameters,such as temperature and pressure,at multiple spatial locations within a grid with high accuracy,utilizing input data from a single station.However,accuracy is slightly compromised when predicting wind speed components.Root mean square error (RMSE) was utilized to report the accuracy of predicted results,with values of 1.453℃for temperature,77 Pa for predicted pressure,1.058 m/s for the wind speed of U-component and 0.959 m/s for the wind speed of V-component.In conclusion,this approach offers a precise,efficient,and wellinformed method for administrative decision-making during nuclear emergencies.

Unconfined compressive strength prediction of soils stabilized using artificial neural networks and support vector machines

This study aims to improve the unconfined compressive strength of soils using additives as well as by predicting the strength behavior of stabilized soils using two artificial-intelligence-based models.The soils used in this study are stabilized using various combinations of cement,lime,and rice husk ash.To predict the results of unconfined compressive strength tests conducted on soils,a comprehensive laboratory dataset comprising 137 soil specimens treated with different combinations of cement,lime,and rice husk ash is used.Two artificial-intelligence-based models including artificial neural networks and support vector machines are used comparatively to predict the strength characteristics of soils treated with cement,lime,and rice husk ash under different conditions.The suggested models predicted the unconfined compressive strength of soils accurately and can be introduced as reliable predictive models in geotechnical engineering.This study demonstrates the better performance of support vector machines in predicting the strength of the investigated soils compared with artificial neural networks.The type of kernel function used in support vector machine models contributed positively to the performance of the proposed models.Moreover,based on sensitivity analysis results,it is discovered that cement and lime contents impose more prominent effects on the unconfined compressive strength values of the investigated soils compared with the other parameters.

NONLINEAR MODELING AND CONTROLLING OF ARTIFICIAL MUSCLE SYSTEM USING NEURAL NETWORKS

The pneumatic artificial muscles are widely used in the fields of medicalrobots, etc. Neural networks are applied to modeling and controlling of artificial muscle system. Asingle-joint artificial muscle test system is designed. The recursive prediction error (RPE)algorithm which yields faster convergence than back propagation (BP) algorithm is applied to trainthe neural networks. The realization of RPE algorithm is given. The difference of modeling ofartificial muscles using neural networks with different input nodes and different hidden layer nodesis discussed. On this basis the nonlinear control scheme using neural networks for artificialmuscle system has been introduced. The experimental results show that the nonlinear control schemeyields faster response and higher control accuracy than the traditional linear control scheme.

Performance Comparison of Artificial Neural Network Models for Daily Rainfall Prediction

With an aim to predict rainfall one-day in advance, this paper adopted different neural network models such as feed forward back propagation neural network （BPN）, cascade-forward back propagation neural network （CBPN）, distributed time delay neural network （DTDNN） and nonlinear autoregressive exogenous network （NARX）, and compared their forecasting capabilities. The study deals with two data sets, one containing daily rainfall, temperature and humidity data of Nilgiris and the other containing only daily rainfall data from 14 rain gauge stations located in and around Coonoor （a taluk of Nilgiris）. Based on the performance analysis, NARX network outperformed all the other networks. Though there is no major difference in the performances of BPN, CBPN and DTDNN, yet BPN performed considerably well confirming its prediction capabilities. Levenberg Marquardt proved to be the most effective weight updating technique when compared to different gradient descent approaches. Sensitivity analysis was instrumental in identifying the key predictors.

Nonlinear model predictive control based on support vector machine and genetic algorithm

This paper presents a nonlinear model predictive control(NMPC) approach based on support vector machine(SVM) and genetic algorithm(GA) for multiple-input multiple-output(MIMO) nonlinear systems.Individual SVM is used to approximate each output of the controlled plant Then the model is used in MPC control scheme to predict the outputs of the controlled plant.The optimal control sequence is calculated using GA with elite preserve strategy.Simulation results of a typical MIMO nonlinear system show that this method has a good ability of set points tracking and disturbance rejection.

Yarn Properties Prediction Based on Machine Learning Method

Although many works have been done to construct prediction models on yarn processing quality,the relation between spinning variables and yarn properties has not been established conclusively so far.Support vector machines(SVMs),based on statistical learning theory,are gaining applications in the areas of machine learning and pattern recognition because of the high accuracy and good generalization capability.This study briefly introduces the SVM regression algorithms,and presents the SVM based system architecture for predicting yarn properties.Model selection which amounts to search in hyper-parameter space is performed for study of suitable parameters with grid-research method.Experimental results have been compared with those of artificial neural network(ANN)models.The investigation indicates that in the small data sets and real-life production,SVM models are capable of remaining the stability of predictive accuracy,and more suitable for noisy and dynamic spinning process.

Machine learning methods for rockburst prediction-state-of-the-art review

One of the most serious mining disasters in underground mines is rockburst phenomena.They can lead to injuries and even fatalities as well as damage to underground openings and mining equipment.This has forced many researchers to investigate alternative methods to predict the potential for rockburst occurrence.However,due to the highly complex relation between geological,mechanical and geometric parameters of the mining environment,the traditional mechanics-based prediction methods do not always yield precise results.With the emergence of machine learning methods,a breakthrough in the prediction of rockburst occurrence has become possible in recent years.This paper presents a state-ofthe-art review of various applications of machine learning methods for the prediction of rockburst potential.First,existing rockburst prediction methods are introduced,and the limitations of such methods are highlighted.A brief overview of typical machine learning methods and their main features as predictive tools is then presented.The current applications of machine learning models in rockburst prediction are surveyed,with related mechanisms,technical details and performance analysis.

Application of Least Square Support Vector Machine (LSSVM) for Determination of Evaporation Losses in Reservoirs

This article adopts Least Square Support Vector Machine (LSSVM) for prediction of Evaporation Losses (EL) in reservoirs. LSSVM is firmly based on the theory of statistical learning, uses regression technique. The input of LSSVM model is Mean air temperature (T) (?C), Average wind speed (WS)(m/sec), Sunshine hours (SH)(hrs/day), and Mean relative humidity(RH)(%). LSSVM has been used to compute error barn of predicted data. An equation has been developed for the determination of EL. Sensitivity analysis has been also performed to investigate the importance of each of the input parameters. A comparative study has been presented between LSSVM and artificial neural network (ANN) models. This study shows that LSSVM is a powerful tool for determination EL in reservoirs.

基于机器学习的地方鸡产蛋曲线拟合探索

[目的]本研究探索以机器学习方法对2个地方鸡品系周产蛋率建模,并将其与非线性回归方法比较,旨在提高养鸡生产中产蛋曲线的拟合精度。[方法]产蛋数据采集自地方鸡杂交组合试验群,自22周龄开始统计产蛋数,至50周龄截止。试验鸡于全封闭鸡舍单笼饲养,产蛋期人工补光16 h。试验鸡分为两组,每组150只鸡。第Ⅰ组是黄羽肉鸡合成系,第Ⅱ组是兼用型地方鸡种。以IBM SPSS Statistics 21.0软件中的非线性回归方法拟合产蛋曲线,所用模型包括Logistic模型、McNally模型、杨宁模型以及Grossman模型。以MATLAB R2014a构建机器学习模型,神经网络选用多层感知器,用300次迭代的拟牛顿法训练数据。以贝叶斯最小二乘支持向量机构建产蛋模型,针对正则项系数和核函数参数进行优化。[结果]依据MSE、R 2、AIC评判标准,Grossman模型在4种非线性回归模型中拟合度最好,McNally模型表现最差。McNally模型预测的高峰产蛋率偏离真实值;Logistic模型、杨宁模型以及Grossman模型高峰产蛋率统计值与真实值基本相符。两组试验鸡的模型参数不同,Ⅱ组持续产蛋能力优于Ⅰ组。基于MSE、R 2以及图形评估结果,神经网络优于传统的非线性方程拟合,而支持向量机略好于神经网络。优化后神经网络参数为2个隐藏层,每个隐藏层包含5个神经元。第Ⅰ组支持向量机的正则项系数为30.97,核参数为0.0701;第Ⅱ组支持向量机的正则项系数为566.53,核参数为0.1754.[结论]机器学习方法可用于产蛋模型构建,相比于传统单变量回归方法,机器学习方法可加入更多变量,提供更准确的预测。

人工神经网络在非线性经济预测中的应用

运用人工神经网络方法 ,以天津市特定时间段工业总产值为样本 ,进行宏观经济模拟预测分析 ,结果证明与其它经济计量方法相比较 ,其预测精度较高 .人工神经网络应用于宏观经济预测具有广泛的实际应用前景 .

机器学习算法在医疗领域中的应用

阐述了机器学习的定义及分类,介绍了决策树、贝叶斯网络、人工神经网络、支持向量机、深度学习等经典算法,重点分析了机器学习在疾病预测、疾病辅助诊断、疾病预后评估、新药研发、健康管理、医学图像识别等医疗领域的应用情况,指出了机器学习在医疗领域的应用还可拓展到病案推理、药物警戒等方面,对于进一步提升整个医疗行业的发展意义重大。

人工神经网络在空气污染预报中的研究进展

人工神经网络是20世纪80年代迅速兴起的一门非线性科学,特别适用于对具有多因素性、不确定性、随机性、非线性等特点的对象进行研究,而空气污染预报正是这样的一类问题。简单介绍了人工神经网络的基本概念,详细地回顾了国内外人工神经网络在空气污染预报领域的研究应用情况,最后讨论了人工神经网络在空气污染预报领域中的研究方向和发展趋势。

基于支持向量机的纺纱质量预测模型研究

纱线的生产是一个多环节的复杂工业过程,其质量控制大多需要依赖领域专家的个人经验,为此提出一种基于支持向量机的纱线质量预测模型.探讨了模型选择与验证问题,并利用“网格搜索”法对模型参数进行了优化.试验结果表明,在小样本和“噪音”数据环境下,支持向量机模型仍能保持一定的预测精度,与人工神经网络模型相比,更适应于真实纺纱生产过程.

水环境非线性时序预测的高精度RBF网络模型

为提高水环境非线性时序预测模型的精度,用自相关技术分析水环境时间序列的延迟特性,确定径向基函数(RBF)网络的输入、输出向量,建立了水环境时间序列预测的高精度RBF网络模型。用32年海洋水温时间序列实测资料来训练和检验网络并用于预测。用该模型对长江流域望江楼站8年总硬度、高锰酸盐指数、五日生化需氧量、氨氮、溶解氧、挥发酚、镉、氯化物、硫酸盐等9种水环境要素时间序列进行预测。实例分析表明,所建模型预测误差均较小,好于门限自回归模型,BP神经网络模型和ELMAN神经网络模型。所建模型不仅精度高,而且收敛速度快。

基于组合预测方法的风电场短期风速预测

基于持续法、人工神经网络法(ANN)和支持向量机(SVM)3种不同预测模型对内蒙古某风电场短期风速进行了预测研究,比较了不同单一预测模型的预测精度,并进行了4种不同预测模型的组合预测。计算结果表明,单一预测模型中支持向量机方法精度最高,而组合预测中3种方法组合的预测精度最高,并且组合预测精度均高于单一预测方法的精度。同时发现,当单一模型预测误差之间存在较强的负相关关系时,组合预测精度提高明显;而当单一模型预测误差之间存在较强的正相关关系时,则组合预测精度改进有限。

煤自燃极限参数的支持向量机预测模型

建立了基于支持向量机(Support Vector Machine,SVM)的煤自燃极限参数预测模型;经过与多项式函数及Sigmoid核函数的对比,选用径向基函数作为SVM核函数;提出了一种SVM参数优化的变步长搜索方法,先在一个大区域根据训练样本均方差的值改变参数搜索步长,找到一个性能好的小区域,在这个小区域中应用网格搜索法找到最优参数,可提高参数搜索速度.实验表明,与人工神经网络模型相比,在样本有限的情况下,基于支持向量机的煤自燃极限参数预测模型预测精度更高、速度更快,说明支持向量机技术在煤自燃极限参数预测中具有实用价值.