Financial Time Series Prediction Based on BP Neural Network

Financial time series are nonlinear, volatile and chaotic. Inspired by quantum computing, this paper proposed a new model, called primary ensemble empirical mode decomposition combined with quantum neural network (PEEMD-QNN) in predicting the stock index. PEEMD-QNN takes the advantages of the PEEMD which retains the main component of modal component and QNN. To demonstrate that our PEEMD-QNN model is robust, we used the new model to predict six major stock index time series in China at a specific time. Detailed experiments are implemented for both of the proposed prediction models, in which empirical mode decomposition combined with QNN (EMD-QNN), QNN and BP neural network are compared. The results demonstrate that the proposed PEEMD-QNN model has higher accuracy than BP neural network, QNN model and EMD-QNN model in stock market prediction.

The recent work in the author's research group on using independent component analysis (ICA) for the analysis and prediction of financial time series is reviewed. ICA belongs to the group of linear transform methods, with the goal to make a transform from the observed signals into a signal space in which the signals are statistically independent. Sometimes independence can be attained, especially in blind source separation in which the observed signals are assumed to be linear mixtures of independent source components. Then the goal of ICA is to invert the unknown mixing operation. Even when independence is not possible, as is often the case in financial time series, the ICA transformation produces useful component signals whose dependence is reduced, and that are nonGaussian with a density allowing sparse coding. The ICA transformation is also related to the temporal structure of the found signals as measured by Kolmogorov complexity or its approximations. The signals are structured and hence may be easier to interpret and predict. After discussing the ICA criterion within the context of linear signal transforms, the FastICA algorithm is reviewed. It is a computationally efficient method for finding a subset or all of the component signals. Then, two financial applications are covered: decomposition of parallel financial time series of weekly sales into basic factors, and prediction of mixture time series by linear combinations of predictions on the ICA components.

This paper presents the prediction of financial time series using an adaptive neural network, which is called the self-organised multilayer perceptrons inspired by the immune algorithm. The simulation results were compared with the multilayer perceptrons and the functional link neural networks. The prediction capability of the various neural networks was tested on ten different data sets; the US/UK exchange rates, the JP/US exchange rate, the US/EU exchange rates, NASDAQO time series, NASDAQC time series, DJIAO time series, DJIAC time series, DJUAO time series, DJUAC time series and the oil price. We carried out two sets of experiments. In the first set, the input data are pre-processed between 0 and 1 and passed to the neural networks as non-stationary data. In the second set of experiments, the non-stationary input signals are transformed into stationary signals and passed to the neural networks. The predictions demonstrated that all neural networks generate profit using stationary data and fail to generate any profit when using non-stationary data. Furthermore, the experiment results for the stationary signals showed that the (SOMLP) outperforms the MLP neural network based on the profit. However, it produced a lower profit when predicting stock price in comparison with the Functional Link neural networks.

At present, the research on BP neural network has achieved good results in many industries and fields, but there are few projects in the application research of mineral resources mining. Under the social background of the rapid development of electronic information technology, BP neural network and GIS technology are combined to carry out research and application, which will provide a new research path for slope deformation monitoring and disaster prevention in mining area. Therefore, in the paper, the key technology of open-pit mine slope deformation automatic monitoring based on BP neural network and GIS technology was put forward. Firstly, the advantages of BP neural network were analyzed and BP neural network was selected as the prediction model of slope deformation. The artificial fish swarm algorithm was used to improve the BP neural network to improve the performance of the model. Based on the analysis and construction of GIS technology, the combination application of BP neural network and GIS technology was discussed. Through practice, the application effect of the technology was verified, and it has good theoretical and practical value

A dynamic neural network (DNN) and a new computationally efficient functional link artificial neural network (CEFLANN) combination optimised with differential evolution (DE) is presented in this paper to predict financial time series like stock price indices and stock return volatilities of two important Indian stock markets, namely the Reliance Industries Limited (RIL), and NIFTY from one day ahead to one month in advance. The DNN comprises a set of 1st order IIR filters for processing the past inputs and their functional expansions and its weights are adjusted using a sliding mode strategy known for its fast convergence and robustness with respect to chaotic variations in the inputs. Extensive computer simulations are carried out to predict simultaneously the stock market indices and return volatilities and it is observed that the simple IIR–based DNN–FLANN model hybridised with DE produces better forecasting accuracies in comparison to the more complicated neural architectures.

Financial time series exhibit a mixture of linear and nonlinear components as indicated by the corresponding lagplots. As we will explain in this paper, certain financial time series can be approximated by increasing functions of a fixed number of time lags or antecedents. This work presents a suitable model for dealing with financial prediction problems, called increasing hybrid morphological-linear perceptron (IHMP). A pseudo-gradient steepest descent method is presented to design the IHMP (learning process), using the back-propagation algorithm and a systematic approach to overcome the problem of nondifferentiability of morphological operations. The learning process includes an automatic phase correction step that is geared at eliminating the time phase distortions that typically occur in financial time series prediction (“random walk dilemma”). Furthermore, we compare the proposed IHMP with other neural and statistical models using three complex nonlinear problems of financial time series prediction.

Objectives: This research study presents a forecasting model that integrates an efficient discrete wavelet transform and a Backpropagation Neural Network (BPNN) for predicting financial time series. Methods/Statistical analysis: The presented model uses the wavelet transform at several time instances based on local smooth B-Spline wavelets of order d(BSd) to decompose the financial time series data. So, an approximation (long-term trends) component and several details (shortterm deviations) components are obtained. Since the details components act as a complementary part of the approximation component, to prepare a prediction model which applies all decomposed components is very advantageous. Therefore, all components are used as smooth input samples of the neural network to forecast the future of the financial time series. Findings: The proposed model is designed to forecast the stock prices of five different companies, and according to the obtained results, the presented model outperforms a conventional model that uses only the approximation component as a wavelet de-noising-based model. The numerical results have shown the prediction accuracy. Applications/Improvements: The proposed model can predict future stock prices better than the de-noised based model in nearly 70%cases. Keywords: B-Spline Wavelets Multiresolution, Back Propagation Neural Network, Financial Time Series, Stock Market Prediction

This paper continues research within the framework of the scientific direction in econophysics at the Department of Information Systems and Mathematical Methods in Economics of the faculty of Economics of PSU. Modeling and prediction of financial time series is quite a perspective area of research, because it allows participants of financial processes to reduce risks and make effective decisions. For example, we could research financial processes with the help of fractal analysis. In the article there is studied and worked out in detail one of the methods of fractal analysis of financial time series – the box-counting method for assessment of the fractal dimension. This method is often used in studies conducted by domestic authors, but the authors do not delve into the characteristics and problems of using the box-counting method for analysis of time series, that means that the answers to the interested questions have not yet been given. The main problem is that, as a rule, the analyzed object in the tasks of applying the box-counting method to time series is a computer image of the plot of series. In the article there is proposed the procedure of adaptation of the box-counting method for assessment of the fractal dimension of time series, the procedure does not require the formation of a computer image of the plot. In the article there is considered following difficulties developed from this adaptation: 1) high sensitivity of the resulting estimation of the dimension to the input parameters of the method (the ratio of the sides of the covered by cells plane with the plot; the used range of lengths of the side of the cell; the number of partitions of the plane into cells); 2) the non-obviousness of choosing the optimal values ​​of these parameters. In the article there are analyzed approaches to the selection of these parameters that were proposed by other authors, and there are determined the most suitable approaches for the adapted box-counting method. Also there are developed unique methods for determining the ratio of the sides of the plane with the plot. In the paper there is written the computer program that implements the developed method, and this program is tested on the generated data. The study obtained the following results. The fact of sensitivity of the adapted box-counting method to input parameters is confirmed, that indicates the high importance of the correct choice of these parameters. According to the study, there is found out inability of the proposed methods of automatic determination the ratio of the sides of the plane in relation to artificial time series. There are obtained the most precise (in a statistical sense) estimates of fractal dimension, those found by means of the adapted box-counting method, with the fixed ratio of the sides 1:1. According to comparing the adapted box-counting method and R/S analysis, there are obtained the most precise estimates by the second method (R/S analysis). Finally in the paper there are formulated the possible directions for further research: 1) comparison of the accuracy of various methods for assessment of the fractal dimension on series of different lengths; 2) comparison of the methods of fractal analysis and p-adic analysis for modeling and prediction of financial time series; 3) determination of the conditions of applicability of various methods; 4) approbation of the developed methods for determining of the ratio of the sides of the plane with the plot on real economic data.

Prediction of financial time series using artificial neural networks has been the subject of many publications, even if the predictability of financial series remains a subject of scientific debate in the financial literature. Facing this difficulty, analysts often consider a large number of exogenous indica- tors, which makes the fitting of neural networks extremely difficult. In this paper, we analyze how to aggregate a large number of indicators in a smaller number using -possibly nonlinear- projection methods. Nonlinear projection methods are shown to be equivalent to the linear Principal Component Analysis when the prediction tool used on the new variables is linear. Furthermore, the computation of the nonlinear projection gives an objective way to evaluate the number of resulting indicators needed for the prediction. Finally, the advantages of nonlinear projection could be further exploited by using a subsequent nonlinear prediction model. The methodology developed in the paper is validated on data from the BEL20 market index, using systematic cross-validation results. Classification Codes: G00, G14.

Enterprise resource planning (ERP) is the managing business system that allows an enterprise of any organization to utilize a collection of integrated applications to manage its business and automate many back office functions related to technology. The selection itself of a suitable ERP is one of the most important parts in the implementation. This paper attempts to use artificial neural networks to choose an ideal ERP for any enterprise. This paper constructs a three-level BP neural network to analyze the principle and model of a suitable ERP. By using the samples to train and inspect the BP neural network, we conclude that the application of BP neural networks is an effective method to forecast suitable ERP. Thus the purpose of this study is to requite mainly three factors among the many others that influence the choice of a suitable ERP. By using statistics in several investigation-filled samples, we can collect a database for many cases that can in return help us create a model that manages the choice of an ideal ERP for the company and reduces the costs of failure.

Objective To explore the differences in clinical indicators of different pathological types of children with hematuria as the main manifestation, and to establish a BP neural network prediction model based on clinical data. Methods The clinical data and renal pathological results of children who were referred to Children′s Hospital of Chongqing Medical University from June 2003 to December 2018 for evaluation of hematuria as the main manifestation were collected, the significant differences in these clinical indicators were analyzed, and a BP neural network model for predicting renal pathology in children with hematuria as the main manifestation was established. Results A total of 438 cases were enrolled in this study, including 232 males and 206 females, with the onset age of (7.00±3.15) years old.According to different clinical manifestations, the children were divided into microscopic hematuria group(179 cases), gross hematuria group(81 cases), microscopic hematuria and proteinuria group (44 cases), and gross hematuria and proteinuria group(134 cases). There were significant differences in sex ratio, onset age, course of disease, inducement, Addis count of urinary red cells, 24-hour proteinuria, blood urea nitrogen, serum creatinine, serum albumin and serum IgA levels among different clinical manifestations (all P< 0.05). Pathological grouping indicated that there were significant differences in sex ratio, onset age, course of disease, family history, Addis count of urinary red cells, 24-hour proteinuria, blood urea nitrogen, serum creatinine, serum albumin, serum IgA and C3 levels among different pathological groups (all P< 0.05). The BP neural network prediction model was then constructed based on the above indicators, and the accuracy of the prediction model was measured to be 61.19% by using the leave one out method. Conclusions By comparing the differences of various indicators under different clinical manifestations and pathological types, a BP neural network prediction model for renal pathology in children with hematuria as the main manifestation is established.The model can accurately predict renal pathology with the help of related indicators, and provides a basis for determining the time of kidney biopsy. Key words: Hematuria; Renal pathology; BP neural network; Prediction

In this paper, a hybrid model (BP-GA) including a back propagation network and a genetic algorithm is used to estimate the thermal conductivity of the nanofluids. The genetic algorithm is used to optimize the initial weight and threshold of BP neural network, so that the optimized BP neural network can better predict the function output. In this study, CuO-ZnO hybrid nanofluids with the mass fraction of 0%, 1%, 2%, and 3% were studied at temperatures of 25, 30, 35, 40, 45, 50, 55, and 60 °C, respectively. The thermal conductivity of the hybrid nanofluids base liquid is 20:80, 40:60, 50:50, 60:40, 80:20, and the mass ratio of CuO-ZnO is 50:50. The mass fraction of CuO-ZnO, the temperature and the mixing ratio of different base liquids were used as input parameters, and the thermal conductivity was the output parameter, forming a 3-input ANN neural network. The predicted thermal conductivity results of BP neural network and genetic algorithm optimized BP neural network (GA-BP) were compared with experimental data. The results illustrate that the BP neural network optimized by genetic algorithm exerts a positive influence on the accuracy and stability of the predicted output.

BP neural network is effective for deaLing with non-Linear mapping which could satisfactorily describe the non-Linear relations between frequency character and diagnosis results. So the de-noising and feature extraction methods of rotating machinery is discussed in this paper, and a diagnosis model for rotating machinery based on BP neural network is proposed and set up. Then, emulates the model of diagnosis of BP neural network through using MATLAB. Experiment results show that the proposed method based on BP neural network has high practical value in the rotating machinery fault diagnosis.

Financial time series prediction using artificial neural network based on Levenberg-Marquardt algorithm

Always being aware of future economic and investment is important and significant for investors, companies, politicians and public people, in order to come to know the future economic perspective and accordingly how particular policies perform. There are different models in the field of prediction of financial time series that we can perform investment with lower risk by using them. Whatever prediction quantity is more accurate, the investment performs with lower risk. The proposed method of this paper is MA-SVR, which is a combination of memetic algorithm and support vector regression. We use memetic computation to estimate support vector regression method parameters and perform prediction with optimisation of SVR method parameters by using memetic algorithm. We evaluate the proposed MA-SVR method by use of prediction error with RMSE criterion, and compare it with other effective algorithms and we show that the proposed method causes improvement in the result from the view point of closeness of predicted quantities to reality.