基于GA-BP的个股超短线预测应用研究

本文将遗传算法(GA)与BP算法相结合的人工神经网络模型学习算法,通过对海信电信(600060)的股票收盘价进行超短线预测研究,该模型通过matlab编程仿真,通过实验证明了股价超短线预测模型的可行性。     

A comparison between Fama and French's model and artificial neural networks in predicting the Chinese stock market

Evidence exists that emerging market stock returns are influenced by a different set of factors than those that influence the returns for stocks traded in developed countries. This study uses artificial neural networks to predict stock price movement (i.e., price returns) for firms traded on the Shanghai stock exchange. We compare the predictive power using linear models from financial forecasting literature to the predictive power of the univariate and multivariate neural network models. Our results show that neural networks outperform the linear models compared. These results are statistically significant across our sample firms, and indicate neural networks are a useful tool for stock price prediction in emerging markets, like China.     

MDT-CNN-LSTM模型的股价预测研究

股价预测一直是投资者在股票市场中关注的焦点.近年来,深度学习技术在这一领域得到广泛应用.在融合卷积神经网络(CNN)和长短时记忆网络(LSTM),构建CNN-LSTM模型的基础上,引入多向延迟嵌入的张量处理技术MDT(mutiway-delay-embedding),对每日股票因子向量进行因子重构,生成汉克尔矩阵,按时...     

基于Pytorch的LSTM模型对股价的分析与预测

股价预测一直都是股票投资者重点关注和重点研究的方向,针对股价具有高度非线性、高噪声、动态性等问题,提出一种基于自组织特征映射(SOM)神经网络和长短期记忆网络(LSTM)共同应用的股价预测方法。第一步聚类,使用python语言实现改进的自组织特征映射神经网络算法,将187支股票分成三类,三类股票以盈利能力大小进行聚类,并且求出每一类所包含的股票代码;第二步预测,基于Pytorch深度学习框架构造长短期记忆网络模型,分别对每一类中随机的3支股票进行股价预测,再通过均方误差和决定系数对预测结果进行评价。结果表明,在使用相同的预测模型对不同盈利能力的股票做股价预测时,盈利能力越大的股票,预测精度越高。此研究可以为投资者筛选出盈利能力更大的股票,并且在提高股价预测精度上也具有一定的贡献。     

State of charge estimation for Li-ion battery based on model from extreme learning machine

Lithium-ion (Li-ion) battery state of charge (SOC) estimation is important for electric vehicles (EVs). The model-based state estimation method using the Kalman filter (KF) variants is studied and improved in this paper. To establish an accurate discrete model for Li-ion battery, the extreme learning machine (ELM) algorithm is proposed to train the model using experimental data. The estimation of SOC is then compared using four algorithms: extended Kalman filter (EKF), unscented Kalman filter (UKF), adaptive extended Kalman filter (AEKF) and adaptive unscented Kalman filter (AUKF). The comparison of the experimental results shows that AEKF and AUKF have better convergence rate, and AUKF has the best accuracy. The comparison from the radial basis function neural network (RBF NN) model also verifies that the ELM model has lighter computation load and smaller estimation error in SOC estimation process. In general, the performance of Li-ion battery SOC estimation is improved by the AUKF algorithm applied on the ELM model.     

A comparative study of artificial neural networks,and decision trees for digital game content stocks price prediction

Precise prediction of stock prices is difficult chiefly because of the many intervening factors. Unpredictability is particularly notable in the aftermath of the global financial crisis. Data mining may however be used to discover highly correlated estimation models. This study looks at artificial neural networks (ANN), decision trees and the hybrid model of ANN and decision trees (hybrid model), the three common algorithm methods used for numerical analysis, to forecast stock prices. The author compared the stock price forecasting models derived from the three methods, and applied the models on 10 different stocks in 320 data sets in an empirical forecast. Average accuracy of ANN is 15.31%, the highest, in terms of match with real market stock prices, followed by decision trees, at 14.06%; hybrid model is 13.75%. The study also discovers that compared to the other two methods, ANN is a more stable method for predicting stock prices in the volatile post-crisis stock market.     

卡尔曼实时跟踪模型在股票价格预测中的应用

基于卡尔曼滤波的动态、实时跟踪性以及股票市场的易波动性,该文提出了将股票视为一个机动物体,其价格视为该物体的位移,其价格的变化视为该物体的速度,依据非线性物理动力学模型来描述股票价格的波动,并且利用卡尔曼滤波理论建立了一种动态的股票价格预测模型,最后给出了相应的算法.通过实例仿真,并对结果进行分析表明,本文提出的算法具有可靠、计算简便、快速等特点,模型预测精度较高,并可实现实时跟踪预测,具有一定的理论价值和实用价值.     

Online State–Space Modeling Using Recurrent Multilayer Perceptrons with Unscented Kalman Filter

A nonlinear black-box modeling approach using a state–space recurrent multilayer perceptron (RMLP) is considered in this paper. The unscented Kalman filter (UKF), which was proposed recently and is appropriate for state–space representation, is employed to train the RMLP. The UKF offers a derivative-free computation and an easy implementation, compared to the extended Kalman filter (EKF) widely used for training neural networks. In addition, the UKF has a fast convergence rate and an excellent capability of parameter estimation which are appropriate for online learning. Through modeling experiments of nonlinear systems, the effectiveness of the RMLP trained with the UKF is demonstrated.     

Online option price forecasting by using unscented Kalman filters and support vector machines

This study develops a hybrid model that combines unscented Kalman filters (UKFs) and support vector machines (SVMs) to implement an online option price predictor. In the hybrid model, the UKF is used to infer latent variables and make a prediction based on the Black–Scholes formula, while the SVM is employed to model the nonlinear residuals between the actual option prices and the UKF predictions. Taking option data traded in Taiwan Futures Exchange, this study examined the forecasting accuracy of the proposed model, and found that the new hybrid model is superior to pure SVM models or hybrid neural network models in terms of three types of options. This model can help investors for reducing their risk in online trading.     

基于Elman神经网络的股票价格预测研究

为了更好地把握股票价格的波动，应用了在处理序列数据输入输出具有优越性的Elman 递归神经网络建立股市预测模型，并用两支股票进行了检测，检测结果说明人工神经网络应用于中国股票市场的预测是可行和有效的，有着良好的前景。     

Application of wrapper approach and composite classifier to the stock trend prediction

The research on the stock market prediction has been more popular in recent years. Numerous researchers tried to predict the immediate future stock prices or indices based on technical indices with various mathematical models and machine learning techniques such as artificial neural networks (ANN), support vector machines (SVM) and ARIMA models. Although some researches in the literature exhibit satisfactory prediction achievement when the average percentage error and root mean square error are used as the performance metrics, the prediction accuracy of whether stock market goes or down is seldom analyzed. This paper employs wrapper approach to select the optimal feature subset from original feature set composed of 23 technical indices and then uses voting scheme that combines different classification algorithms to predict the trend in Korea and Taiwan stock markets. Experimental result shows that wrapper approach can achieve better performance than the commonly used feature filters, such as χ²-Statistic, Information gain, ReliefF, Symmetrical uncertainty and CFS. Moreover, the proposed voting scheme outperforms single classifier such as SVM, kth nearest neighbor, back-propagation neural network, decision tree, and logistic regression.     

Online identification of nonlinear multivariable processes using self‐generating RBF neural networks

This paper addresses the problem of online model identification for multivariable processes with nonlinear and time‐varying dynamic characteristics. For this purpose, two online multivariable identification approaches with self‐organizing neural network model structures will be presented. The two adaptive radial basis function (RBF) neural networks are called as the growing and pruning radial basis function (GAP‐RBF) and minimal resource allocation network (MRAN). The resulting identification algorithms start without a predefined model structure and the dynamic model is generated autonomously using the sequential input‐output data pairs in real‐time applications. The extended Kalman filter (EKF) learning algorithm has been extended for both of the adaptive RBF‐based neural network approaches to estimate the free parameters of the identified multivariable model. The unscented Kalman filter (UKF) has been proposed as an alternative learning algorithm to enhance the accuracy and robustness of nonlinear multivariable processes in both the GAP‐RBF and MRAN based approaches. In addition, this paper intends to study comparatively the general applicability of the particle filter (PF)‐based approaches for the case of non‐Gaussian noisy environments. For this purpose, the Unscented Particle Filter (UPF) is employed to be used as alternative to the EKF and UKF for online parameter estimation of self‐generating RBF neural networks. The performance of the proposed online identification approaches is evaluated on a highly nonlinear time‐varying multivariable non‐isothermal continuous stirred tank reactor (CSTR) benchmark problem. Simulation results demonstrate the good performances of all identification approaches, especially the GAP‐RBF approach incorporated with the UKF and UPF learning algorithms. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Evolutional RBFNs prediction systems generation in the applications of financial time series data

An artificial neural prediction system is automatically developed with the combinations of step wise regression analysis (SRA), dynamic learning and recursive-based particle swarm optimization (RPSO) learning algorithms. In the first stage, the SRA can be considered like a data filtering machine to choose two primary factors from 20 channel technical indexes as input variables of the RBFNs system. Then, an efficient dynamic learning algorithm is applied to sequentially generate RBFs functions from training data set, where it can efficiently determine the proper number of RBFs’ centers and their associated positions. It can be exploited to forecast appropriate behaviors of the wanted identified financial time series data. While characteristics of training data set are automatically mined and generated by the proposed dynamic learning algorithm, architecture of the RBFNs prediction system is initially represented with collected information. Moreover, the RPSO learning scheme with the hybrid particle swarm optimization (PSO) and recursive least-squares (RLS) learning methods are applied to extract those appropriate parameters of the RBFNs prediction system.The RBFNs prediction systems are implemented in data analysis, module generation and price trend of the financial time series data. It not only automatically determines proper RBFs number but also fast approach the desired target in actual trading of Taiwan stock index (TAIEX). Computer simulations in training and testing phases of historic TAIEX are compared with other learning methods, which illustrate our great performance not only increases the accuracy of the stock price prediction but also improves the win rate in the trend of TAIEX.     

Empirical analysis: stock market prediction via extreme learning machine

How to predict stock price movements based on quantitative market data modeling is an attractive topic. In front of the market news and stock prices that are commonly believed as two important market data sources, how to extract and exploit the hidden information within the raw data and make both accurate and fast predictions simultaneously becomes a challenging problem. In this paper, we present the design and architecture of our trading signal mining platform that employs extreme learning machine (ELM) to make stock price prediction based on those two data sources concurrently. Comprehensive experimental comparisons between ELM and the state-of-the-art learning algorithms, including support vector machine (SVM) and back-propagation neural network (BP-NN), have been undertaken on the intra-day tick-by-tick data of the H-share market and contemporaneous news archives. The results have shown that (1) both RBF ELM and RBF SVM achieve higher prediction accuracy and faster prediction speed than BP-NN; (2) the RBF ELM achieves similar accuracy with the RBF SVM and (3) the RBF ELM has faster prediction speed than the RBF SVM. Simulations of a preliminary trading strategy with the signals are conducted. Results show that strategy with more accurate signals will make more profits with less risk.     

A comparison of simultaneous state and parameter estimation schemes for a continuous fermentor reactor

This article proposes a maximum likelihood algorithm for simultaneous estimation of state and parameter values in nonlinear stochastic state-space models. The proposed algorithm uses a combination of expectation maximization, nonlinear filtering and smoothing algorithms. The algorithm is tested with three popular techniques for filtering namely particle filter (PF), unscented Kalman filter (UKF) and extended Kalman filter (EKF). It is shown that the proposed algorithm when used in conjunction with UKF is computationally more efficient and provides better estimates. An online recursive algorithm based on nonlinear filtering theory is also derived and is shown to perform equally well with UKF and ensemble Kalman filter (EnKF) algorithms. A continuous fermentation reactor is used to illustrate the efficacy of batch and online versions of the proposed algorithms.     

A novel hybrid stock selection method with stock prediction

The success of stock selection is contingent upon the future performance of stock markets. We incorporate stock prediction into stock selection to specifically capture the future features of stock markets, thereby forming a novel hybrid (two-step) stock selection method (involving stock prediction and stock scoring). (1) Stock returns for the next period are predicted using emerging computational intelligence (CI), i.e., extreme learning machine with a powerful learning capacity and a fast computing speed. (2) A stock scoring mechanism is developed as a linear combination of the predicted factor (generated in the first step) and the fundamental factors (popular in existing literature) based on CI-based optimization for weights, and top-ranked stocks are selected for an equally weighted portfolio. Using the A-share market of China as the study sample, the empirical results show that the novel hybrid approach, using highly weighted predicted factors, statistically outperforms both traditional methods (without stock prediction) and similar counterparts (with other model designs) in terms of market returns, which suggests the great contribution of stock prediction for improving stock selection.     

SSUKF-WNN算法及其在飞行器气动力建模中的应用

针对传统小波网络算法的不足,提出一种基于改进无迹Kalman滤波(UKF)的小波网络算法.该算法使用一种基于简化球形分布Sigma点的UKF(SSUKF)来训练小波网络的参数,以提高小波网络的学习性能和训练质量.飞行器气动力建模算例表明,相对于BP算法和EKF算法,SSUKF算法训练的小波网络收敛速度更快,估计精度更高,计算量更小.同时也为飞行器的气动力建模提供了一种有效可行的手段.     

基于改进回声状态神经网络的个股股价预测

当今社会股价预测是研究的热门问题,人们越来越关注对股价预测模型的建立,提高股价预测的精度对股票投资者有实际的应用价值.目前股价的预测方法层出不穷,其中较为典型的有传统的技术分析和ARMA模型等.为了提升预测的精度,同时考虑到股市的非线性,本文提出一种改进的回声状态神经网络的个股股价预测模型,针对回声状态神经网络(ESN)泛化能力不强的特点,应用改进的粒子群算法(GTPSO)对回声状态神经网络(ESN)的输出连接权进行搜索,最终得到最优解,即ESN的最优输出连接权, GTPSO算法概括来说就是在传统粒子群算法(PSO)的基础上引入禁忌搜索算法(TS)中禁忌的思想和遗传算法(GA)中变异的思想,从而降低PSO在学习过程中陷入局部最小值的状况,同时提高PSO搜寻全局的能力.将预测模型用于个股每日收盘价预测中,使用每10天的收盘价预测第11天的收盘价.通过实验验证了模型的正确性,实验证实,该模型拥有较好的预测效果.     

An adaptive fuzzy neural network for MIMO system modelapproximation in high-dimensional spaces

An adaptive fuzzy system implemented within the framework of neural network is proposed. The integration of the fuzzy system into a neural network enables the new fuzzy system to have learning and adaptive capabilities. The proposed fuzzy neural network can locate its rules and optimize its membership functions by competitive learning, Kalman filter algorithm and extended Kalman filter algorithms. A key feature of the new architecture is that a high dimensional fuzzy system can be implemented with fewer number of rules than the Takagi-Sugeno fuzzy systems. A number of simulations are presented to demonstrate the performance of the proposed system including modeling nonlinear function, operator's control of chemical plant, stock prices and bioreactor (multioutput dynamical system).     

一种针对全海深载人潜水器的异步融合组合导航算法

全海深载人潜水器(HOV)组合导航中会产生异步融合现象,传统的组合导航算法在处理时会产生较大的误差.针对这一问题,提出了一种基于机器学习和无迹卡尔曼滤波(UKF)的异步融合组合导航算法.首先建立了针对超短基线(USBL)声学定位系统预测的机器学习模型,通过USBL声学定位系统的观测数据集来训练该模型,并用得到的模型来预测更新间隔内的数据.最后使用UKF将已更新的数据集进行融合.仿真结果表明,相比传统的组合导航算法,本文的异步融合组合导航算法可以将USBL声学定位系统数据异步问题所引起的误差降低17%,有效提高了组合导航系统的精度.