An improved sales forecasting approach by the integration of genetic fuzzy systems and data clustering: Case study of printed circuit board

Success in forecasting and analyzing sales for given goods or services can mean the difference between profit and loss for an accounting period and, ultimately, the success or failure of the business itself. Therefore, reliable prediction of sales becomes a very important task. This article presents a novel sales forecasting approach by the integration of genetic fuzzy systems (GFS) and data clustering to construct a sales forecasting expert system. At first, all records of data are categorized into k clusters by using the K-means model. Then, all clusters will be fed into independent GFS models with the ability of rule base extraction and data base tuning. In order to evaluate our K-means genetic fuzzy system (KGFS) we apply it on a printed circuit board (PCB) sales forecasting problem which has been used as the case in different studies. We compare the performance of an extracted expert system with previous sales forecasting methods using mean absolute percentage error (MAPE) and root mean square error (RMSE). Experimental results show that the proposed approach outperforms the other previous approaches.     

Enhanced stock price variation prediction via DOE and BPNN-based optimization

Stock price variation predictions are at the core of many research issues, and neural networks (NNs) are widely applied and were proven to be more efficient than time series forecasting for stock price forecasting. However, this type of research always determines the parameter settings of the NNs rationally through a trial-and-error methodology. This paper integrates design of experiment (DOE), Taguchi method, and back-propagation NN (BPNN) to construct a robust engine to further optimize the prediction accuracy under a robust DOE-based predictor. Adopting data from Taiwan Stock Exchange (TWSE), the technical analytical indexes and β value of the listed stocks of TWSE were computed. The research results indicated that the proposed approach can effectively improve the forecasting rate of stock price variations.     

模糊组合神经网络智能选股模型的建立

针对传统的股票市场预测模型,为了准确地预测股票价格趋势、为广大投资者规避风险,应用模糊逻辑和组合神经网络,利用贝叶斯统计学与组合理论使二者有机结合,提出一种股票市场建模及预测方法。组合神经网络结合BP网络和径向基函数网络(RBF),神经元模糊系统有更强的学习和推理机制,能避免黑箱问题。实证研究结果表明,该方法有较高的预测精度和更好的稳定性。     

Support vector regression with chaos-based firefly algorithm for stock market price forecasting

Due to the inherent non-linearity and non-stationary characteristics of financial stock market price time series, conventional modeling techniques such as the Box–Jenkins autoregressive integrated moving average (ARIMA) are not adequate for stock market price forecasting. In this paper, a forecasting model based on chaotic mapping, firefly algorithm, and support vector regression (SVR) is proposed to predict stock market price. The forecasting model has three stages. In the first stage, a delay coordinate embedding method is used to reconstruct unseen phase space dynamics. In the second stage, a chaotic firefly algorithm is employed to optimize SVR hyperparameters. Finally in the third stage, the optimized SVR is used to forecast stock market price. The significance of the proposed algorithm is 3-fold. First, it integrates both chaos theory and the firefly algorithm to optimize SVR hyperparameters, whereas previous studies employ a genetic algorithm (GA) to optimize these parameters. Second, it uses a delay coordinate embedding method to reconstruct phase space dynamics. Third, it has high prediction accuracy due to its implementation of structural risk minimization (SRM). To show the applicability and superiority of the proposed algorithm, we selected the three most challenging stock market time series data from NASDAQ historical quotes, namely Intel, National Bank shares and Microsoft daily closed (last) stock price, and applied the proposed algorithm to these data. Compared with genetic algorithm-based SVR (SVR-GA), chaotic genetic algorithm-based SVR (SVR-CGA), firefly-based SVR (SVR-FA), artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFIS), the proposed model performs best based on two error measures, namely mean squared error (MSE) and mean absolute percent error (MAPE).     

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.     

Stock trend prediction based on a new status box method and AdaBoost probabilistic support vector machine

Stock trend prediction is regarded as one of the most challenging tasks of financial time series prediction. Conventional statistical modeling techniques are not adequate for stock trend forecasting because of the non-stationarity and non-linearity of the stock market. With this regard, many machine learning approaches are used to improve the prediction results. These approaches mainly focus on two aspects: regression problem of the stock price and prediction problem of the turning points of stock price. In this paper, we concentrate on the evaluation of the current trend of stock price and the prediction of the change orientation of the stock price in future. Then, a new approach named status box method is proposed. Different from the prediction issue of the turning points, the status box method packages some stock points into three categories of boxes which indicate different stock status. And then, some machine learning techniques are used to classify these boxes so as to measure whether the states of each box coincides with the stock price trend and forecast the stock price trend based on the states of the box. These results would support us to make buying or selling strategies. Comparing with the turning points prediction that only considered the features of one day, each status box contains a certain amount of points which represent the stock price trend in a certain period of time. So, the status box reflects more information of stock market. To solve the classification problem of the status box, a special features construction approach is presented. Moreover, a new ensemble method integrated with the AdaBoost algorithm, probabilistic support vector machine (PSVM), and genetic algorithm (GA) is constructed to perform the status boxes classification. To verify the applicability and superiority of the proposed methods, 20 shares chosen from Shenzhen Stock Exchange (SZSE) and 16 shares from National Association of Securities Dealers Automated Quotations (NASDAQ) are applied to perform stock trend prediction. The results show that the status box method not only have the better classification accuracy but also effectively solve the unbalance problem of the stock turning points classification. In addition, the new ensemble classifier achieves preferable profitability in simulation of stock investment and remarkably improves the classification performance compared with the approach that only uses the PSVM or back-propagation artificial neural network (BPN).     

A hybrid ANFIS based on n-period moving average model to forecast TAIEX stock

Linear model is a general forecasting model and moving average technical index (MATI) is one of useful forecasting methods to predict the future stock prices in stock markets. Therefore, individual investors, stock fund managers, and financial analysts attempt to predict price fluctuation in stock markets by either linear model or MATI. From literatures, three major drawbacks are found in many existing forecasting models. First, forecasting rules mined from some AI algorithms, such as neural networks, could be very difficult to understand. Second, statistic assumptions about variables are required for time series to generate forecasting models, which are not easily understandable by stock investors. Third, stock market investors usually make short-term decisions based on recent price fluctuations, i.e., the last one or two periods, but most time series models use only the last period of stock price. In order to overcome these drawbacks, this study proposes a hybrid forecasting model using linear model and MATI to predict stock price trends with the following four steps: (1) test the lag period of Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and calculate the last n-period moving average; (2) use subtractive clustering to partition technical indicator values into linguistic values based on data discretization method objectively; (3) employ fuzzy inference system (FIS) to build linguistic rules from the linguistic technical indicator dataset, and optimize the FIS parameters by adaptive network; and (4) refine the proposed model by adaptive expectation models. The proposed model is then verified by root mean squared error (RMSE), and a ten-year period of TAIEX is selected as experiment datasets. The results show that the proposed model is superior to the other forecasting models, namely Chen's model and Yu's model in terms of RMSE.     

A novel model by evolving partially connected neural network for stock price trend forecasting

This paper proposes a novel model by evolving partially connected neural networks (EPCNNs) to predict the stock price trend using technical indicators as inputs. The proposed architecture has provided some new features different from the features of artificial neural networks: (1) connection between neurons is random; (2) there can be more than one hidden layer; (3) evolutionary algorithm is employed to improve the learning algorithm and training weights. In order to improve the expressive ability of neural networks, EPCNN utilizes random connection between neurons and more hidden layers to learn the knowledge stored within the historic time series data. The genetically evolved weights mitigate the well-known limitations of gradient descent algorithm. In addition, the activation function is defined using sin(x) function instead of sigmoid function. Three experiments were conducted which are explained as follows. In the first experiment, we compared the predicted value of the trained EPCNN model with the actual value to evaluate the prediction accuracy of the model. Second experiment studied the over fitting problem which occurred in neural network training by taking different number of neurons and layers. The third experiment compared the performance of the proposed EPCNN model with other models like BPN, TSK fuzzy system, multiple regression analysis and showed that EPCNN can provide a very accurate prediction of the stock price index for most of the data. Therefore, it is a very promising tool in forecasting of the financial time series data.     

Fusion ANFIS models based on multi-stock volatility causality for TAIEX forecasting

Stock market investors value accurate forecasting of future stock price from trading systems because of the potential for large profits. Thus, investors use different forecasting models, such as the time-series model, to assemble a superior investment portfolio. Unfortunately, there are three major drawbacks to the time-series model: (1) most statistical methods rely on some assumptions about the variables; (2) most conventional time-series models use only one variable in forecasting; and (3) the rules mined from artificial neural networks are not easily understandable. To address these shortcomings, this study proposes a new model based on multi-stock volatility causality, a fusion adaptive-network-based fuzzy inference system (ANFIS) procedure, for forecasting stock price problems in Taiwan. Furthermore, to illustrate the proposed model, three practical, collected stock index datasets from the USA and Taiwan stock markets are used in the empirical experiment. The experimental results indicate that the proposed model is superior to the listing methods in terms of root mean squared error, and further evaluation reveals that the profits comparison results for the proposed model produce higher profits than the listing models.     

High-precision forecast using grey models

In recent years the grey theorem has been successfully used in many prediction applications. The proposed Markov-Fourier grey model prediction approach uses a grey model to predict roughly the next datum from a set of most recent data. Then, a Fourier series is used to fit the residual error produced by the grey model. With the Fourier series obtained, the error produced by the grey model in the next step can be estimated. Such a Fourier residual correction approach can have a good performance. However, this approach only uses the most recent data without considering those previous data. In this paper, we further propose to adopt the Markov forecasting method to act as a longterm residual correction scheme. By combining the short-term predicted value by a Fourier series and a long-term estimated error by the Markov forecasting method, our approach can predict the future more accurately. Three time series are used in our demonstration. They are a smooth functional curve, a curve for the stock market and the Mackey-Glass chaotic time series. The performance of our approach is compared with different prediction schemes, such as back-propagation neural networks and fuzzy models. All these methods are one-step-ahead forecasting. The simulation results show that our approach can predict the future more accurately and also use less computational time than other methods do.     

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.     

基于遗传算法和神经网络的股票价格预测

针对证券市场运作的复杂性,提出了一种改进的BP神经网络模型,并将其应用于金融街的股价预测。采用遗传算法对网络结构和权值进行了优化,提高了网络的预测精度,加快了收敛速度,克服了以往传统预测方法的缺点。实验结果表明,将改进的BP网络模型用于股市分析和股价预测具有一定的准确性和应用价值。     

基于MPCA-RBF模型的证券市场指数时间序列预测*

针对证券市场指数内部结构的复杂性和影响因素的高维性,提出基于MPCA-RBF（多线性主成分分析法-径向基神经网络）模型的证券市场指数时间序列预测方法。由于证券市场间存在关联性,选取了7个证券市场及34个技术指标构建三维张量模型,采用张量方法—MPCA进行特征提取,使降维的同时充分保留数据内部结构,之后利用RBF神经网络进行回归预测,提高了预测精度。对恒生指数和日经225指数的实验结果显示,与非张量模型相比,该模型预测误差较小,预测精度有较显著的提高,表明该模型能充分地保留证券时间序列内部结构,证明了其在证券预测领域的有效性和实用性。     

Combining nonlinear independent component analysis and neural network for the prediction of Asian stock market indexes

With the economic successes of several Asian economies and their increasingly important roles in the global financial market, the prediction of Asian stock markets has becoming a hot research area. As Asian stock markets are highly dynamic and exhibit wide variation, it may more realistic and practical that assumed the stock indexes of Asian stock markets are nonlinear mixture data. In this research, a time series prediction model by combining nonlinear independent component analysis (NLICA) and neural network is proposed to forecast Asian stock markets. NLICA is a novel feature extraction technique to find independent sources from observed nonlinear mixture data where no relevant data mixing mechanisms are available. In the proposed method, we first use NLICA to transform the input space composed of original time series data into the feature space consisting of independent components representing underlying information of the original data. Then, the ICs are served as the input variables of the neural network to build prediction model. Among the Asian stock markets, Japanese and China’s stock markets are the biggest two in Asia and they respectively represent the two types of stock markets. Therefore, in order to evaluate the performance of the proposed approach, the Nikkei 225 closing index and Shanghai B-share closing index are used as illustrative examples. Experimental results show that the proposed forecasting model not only improves the prediction accuracy of the neural network approach but also outperforms the three comparison methods. The proposed stock index prediction model can be therefore a good alternative for Asian stock market indexes.     

基于PCA和IFOA-BP神经网络的股价预测模型

针对基于BP神经网络的股票价格预测模型在价格预测时存在较大误差的问题,在BP神经网络方法的基础上引入了主成分分析方法(PCA)和改进的果蝇算法(IFOA),提出一种基于PCA-IFOA-BP神经网络的股票价格预测模型。通过PCA对股票历史数据进行降维,减少冗余信息;采用改进的果蝇算法优化BP神经网络的初始权值和阈值;建立基于PCA和IFOA-BP神经网络的股票价格预测模型。对上证指数股票价格数据进行仿真验证,仿真结果表明:在股票价格预测中,该模型比BP神经网络、PCA-BP和PCA-FOA-BP的预测精度更高,是一种有效可行的预测方法。     

A hybrid ANFIS model based on empirical mode decomposition for stock time series forecasting

Time series forecasting is an important and widely popular topic in the research of system modeling, and stock index forecasting is an important issue in time series forecasting. Accurate stock price forecasting is a challenging task in predicting financial time series. Time series methods have been applied successfully to forecasting models in many domains, including the stock market. Unfortunately, there are 3 major drawbacks of using time series methods for the stock market: (1) some models can not be applied to datasets that do not follow statistical assumptions; (2) most time series models that use stock data with a significant amount of noise involutedly (caused by changes in market conditions and environments) have worse forecasting performance; and (3) the rules that are mined from artificial neural networks (ANNs) are not easily understandable.To address these problems and improve the forecasting performance of time series models, this paper proposes a hybrid time series adaptive network-based fuzzy inference system (ANFIS) model that is centered around empirical mode decomposition (EMD) to forecast stock prices in the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and Hang Seng Stock Index (HSI). To measure its forecasting performance, the proposed model is compared with Chen's model, Yu's model, the autoregressive (AR) model, the ANFIS model, and the support vector regression (SVR) model. The results show that our model is superior to the other models, based on root mean squared error (RMSE) values.     

AN ADAPTIVE EXPECTATION GENETIC ALGORITHM BASED ON ANFIS AND MULTINATIONAL STOCK MARKET VOLATILITY CAUSALITY FOR TAIEX FORECASTING

The stock market is a highly complex and dynamic system, and forecasting stock is complicated and difficult. Successful prediction of stock prices may promise attractive benefits; therefore, stock market forecasting is important and of great interest. The economy of Taiwan relies on international trade deeply and the fluctuations of international stock markets impact Taiwan's stock market to certain degree. It is practical to use the fluctuations of other stock markets as forecasting factors for forecasting on the Taiwan stock market. Further, stock market investors usually make short-term decisions based on recent price fluctuations, but most time series models use only the last period of stock price in forecasting. In this article, the proposed model uses the fluctuations of other national stock markets as forecasting factors and employs an expectation equation method whose parameters are optimized by a genetic algorithm (GA) joined with an adaptive network–based fuzzy inference system (ANFIS) model to forecast the Taiwan stock index. To evaluate the forecasting performance, the proposed model is compared with Chen's model and Yu's model. The experimental results indicate that the proposed model is superior to the listing methods (Chen's model and Yu's model) in terms of root mean squared error (RMSE).     

Simultaneous optimization of artificial neural networks for financial forecasting

Artificial neural networks (ANNs) have been popularly applied for stock market prediction, since they offer superlative learning ability. However, they often result in inconsistent and unpredictable performance in the prediction of noisy financial data due to the problems of determining factors involved in design. Prior studies have suggested genetic algorithm (GA) to mitigate the problems, but most of them are designed to optimize only one or two architectural factors of ANN. With this background, the paper presents a global optimization approach of ANN to predict the stock price index. In this study, GA optimizes multiple architectural factors and feature transformations of ANN to relieve the limitations of the conventional backpropagation algorithm synergistically. Experiments show our proposed model outperforms conventional approaches in the prediction of the stock price index.     

基于DMD-LSTM模型的股票价格时间序列预测研究

针对股票市场关系复杂导致的有效特征提取困难、价格预测精度低等问题,提出一种基于动态模态分解—长短期记忆神经网络（DMD-LSTM）的股票价格时间序列预测方法。首先通过DMD算法对受市场板块联动效应影响的关联行业板块样本股数据进行分解计算,提取包含整体市场和特定股票走势变化信息的模态特征;然后针对不同市场背景,采用LSTM网络对基本面数据和模态特征进行价格建模预测。在鞍钢股份（SH000898）上的实验结果表明,该方法相较于传统预测方法,在特定的市场背景下能实现更高的价格预测精度,更为准确地描述股票价格的变化规律。     

ARIMA和RBF神经网络相融合的股票价格预测研究

针对股票价格的突变性、非线性和随机性,单一预测方法仅能描述股票价格片断信息等缺陷,提出一种股票价格组合预测模型。采用自回归移动平均模型（ARIMA）对股票价格进行预测,捕捉股票价格线性变化趋势。采用RBF神经网络对非线性、随机变化规律进行预测。将两者结果组合得到股票价格预测结果。采用组合模型对包钢股份（600010）股票收盘价进行仿真实验,结果表明,相对于单一预测模型,组合预测模型更加全面、准确刻画了股票价格的变化规律,提高了股票价格预测精度。