Financial time series prediction by a hybrid memetic computation-based support vector regression (MA-SVR) method

In hydrological research, accurate rainfall data is the primary subject for the minimization of potential loss of life and property that is mainly caused by floods. However, there is a difficulty in getting precise rainfall data for poorly gauged locations, especially in mountainous areas. Weather radar instruments can be the remedy accompanied by some errors. And, these errors should be removed before the implementation of this product. This paper presents the results of the research on radar rainfall estimate errors with support vector regression (SVR) method using the observed rain gauge data. The paper depicts the methodological base of the algorithm that covers additive and multiplicative corrections and the results of practical implementations considering the locations of gauge measurements. The preliminary results show that the SVR has a location-oriented performance. The multiplicative and additive correction factors show decreasing and polynomial trends respectively, as the distance from the radar location increase. Another particular outcome is that the SVR shows better results for the stations located in the mid-range (mainly for 40-60 km) contrary to the nearest ones. Since the systematic error in the radar data is nonlinear, the SVR method would show a promising result with a combination of other optimization techniques.

GMRVV m–SVR model for financial time series forecasting

Rotor-to-stator impact-rub of rotor is a kind of common fault of steam turbine. Traditional method of fault modeling, such as statistical theory and artificial neural network, usually gets a non-linear model because of the complexity of turbine system. Based on the need of analysis for impact-rub degree and fault trend, Support Vector Regression (SVR) arithmetic is imported and used for time series analysis and prediction. SVR suggests a best tradeoff between complexity of model and learning ability to establish a linear model in high-dimension feature space. The model built by SVR is able to reflect the implicit mechanism in the data set of time series. But SVR arithmetic has longer running time and large need of memory, so an improved arithmetic of SVR was imported, which uses smooth method and advances operation capability of standard SVR method, and is very fit for the time series of small sample size. Simulation indicates that smooth SVR method has higher speed, better precision and generalization ability than neutral network, and is effective to analyze the trend of rotor-to-stator impact-rub fault for steam turbine, which has guiding significance for forecasting fault and maintenance of steam turbine.

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.

1: Architectures.- Scale Independence in the Visual System.- Dynamic Neuronal Information Processing of Vowel Sounds in Auditory Cortex.- Convolutional Spiking Neural Network for Robust Object Detection with Population Code using Structured Pulse Packets.- Networks Constructed of Neuroid Elements Capable of Temporal Summation of Signals.- Predictive Synchrony Organized by Spike-Based Hebbian Learning with Time-Representing Synfire Activities.- Improving Chow-Liu Tree Performance by Mining Association Rules.- A Reconstructed Missing Data-Finite Impulse Response Selective Ensemble (RMD-FSE) Network.- Higher Order Multidirectional Associative Memory with Decreasing Energy Function.- Fast Indexing of Codebook Vectors Using Dynamic Binary Search Trees with Fat Decision Hyperplanes.- 2: Learning Algorithms.- On Some External Characteristics of Brain-like Learning and Some Logical Flaws of Connectionism.- Superlinear Learning Algorithm Design.- Extension of Binary Neural Networks for Multi-class Output and Finite Automata.- A Memory-Based Reinforcement Learning Algorithm to Prevent Unlearning in Neural Networks.- Structural Optimization of Neural Networks by Genetic Algorithm with Degeneration (GAd).- Adaptive Training for Combining Classifier Ensembles.- Combination Strategies for Finding Optimal Neural Network Architecture and Weights.- 3: Applications.- Biologically Inspired Recognition System for Car Detection from Real-Time Video Streams.- Financial Time Series Prediction Using Non-Fixed and Asymmetrical Margin Setting with Momentum in Support Vector Regression.- A Method for Applying Neural Networks to Control of Nonlinear Systesm.- Robot Manipulator Control via Recurrent Neural Networks.- Gesture Recognition Based on SOM Using Multiple Sensors.- Enhanced Phrase-Based Document Clustering Using Self-Organizing Map (SOM) Architectures.- Discovering Gene Regulatory Networks from Gene Expression Data with the Use of Evolving Connectionist Systems.- Experimental Analysis of Knowledge Based Multiagent Credit Assignment.- Implementation of Visual Tracking System Using Artificial Retina Chip and Shape Memory Alloy Actuator.

PARP1 is one of six enzymes required for the highly error-prone DNA repair pathway microhomology-mediated end joining (MMEJ) and needs to be inhibited when over-expressed. In order to study the PARP1 inhibitory effect of fused tetracyclic or pentacyclic dihydrodiazepinoindolone derivatives (FTPDDs) by quantitative structure-activity relationship technique, six models were established by four kinds of methods, heuristic method, gene expression programming, random forester, and support vector regression with single, double, and triple kernel function respectively. The single, double, and triple kernel functions were RBF kernel function, the integration of RBF and polynomial kernel functions, and the integration of RBF, polynomial, and linear kernel functions respectively. The problem of multi-parameter optimization introduced in the support vector regression model was solved by the particle swarm optimization algorithm. Among the models, the model established by support vector regression with triple kernel function, in which the optimal R2 and RMSE of training set and test set were 0.9353, 0.9348 and 0.0157, 0.0288, and R2cv of training set and test set were 0.9090 and 0.8971, shows the strongest prediction ability and robustness. The method of support vector regression with triple kernel function is a great promotion in the field of quantitative structure-activity relationship, which will contribute a lot to designing and screening new drug molecules. The information contained in the model can provide important factors that guide drug design. Based on these factors, six new FTPDDs have been designed. Using molecular docking experiments to determine the properties of new derivatives, the new drug was ultimately successfully designed.

Wind energy is a promising alternativefor renewable source of energy pursued world-wide to reduce carbon emissions for a green future. The prediction of wind speed is a challenging subject and plays an instrumental role in development of wind power systems (particularly grid connected renewable energy systems where predicting wind speed facilitates manipulation of the load on the grid). Modern machine learning techniques including neural networks have been widely utilized for this purpose. Literature indicates availability of several models for estimation of the wind speed one hour ahead and the hourly wind speed data profile one day ahead. This paper considers the prediction of wind energy as a univariate time series (UVT) prediction problem and employs major prediction algorithms including the K-Nearest Neighbors (kNN), Random Forest (RF), Support Vector Regression (SVR), Holt-Winter and ARIMA method. Forecasting a univariate time series depends only on past wind speed data values, rather than use of external data attributes like wind direction or weather forecast for prediction algorithm. In the present study (as a case-study), 13 years of hourly average wind speed data (of the period 1970-1982) of Yanbu, Saudi Arabia has been utilized to evaluate the performance of selected algorithms. Yanbu is an industrial city that plays a major role in the economy of Saudi Arabia. The findings showed that SVR, RF and ARIMA methods exhibit a better forecastingperformance in relation to four evaluation parameters of Mean Absolute Percentage Error(MAPE),Symmetric Mean Absolute Percentage Error (sMAPE),Mean Absolute Error (MAE) and Mean Absolute Scaled Error (MASE).

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.

Multiple sources and multiple measures based traffic flow prediction using the chaos theory and support vector regression method

The state of health and remaining useful life of lithium-ion batteries are key indicators for the normal operation of electrical devices. To address the problem of the capacity of lithium-ion batteries being difficult to measure online, in this paper, we propose an online method based on particle swarm optimization and support vector regression to estimation the state of health and remaining useful life. First, a novel health indicator is extracted from the discharge voltage to characterize the capacity of lithium-ion batteries. Then, based on the capacity degradation characteristics, support vector regression is used to predict the remaining useful life of these batteries, and particle swarm optimization is selected to optimize the parameters of the support vector regression, which effectively enhances the predictive performance of the model. Validated for the NASA battery aging dataset, when training with the first 40% of the dataset, the maximum error of the predicted remaining useful life was four cycles, and when training with the first 50% of the dataset, the maximum error of the predicted remaining useful life was only one cycle. When comparing to a deep neural network, support vector regression, long short-term memory algorithms and existing similar methods in the literature, the particle swarm optimization and support vector regression method can obtain more accurate prediction results.

This study aims to predict the sales of electrical energy of PT PLN (Persero) Greater Jakarta Distribution Unit by using machine learning methods, specifically Long Short-Term Memory (LSTM) and Support Vector Regression (SVR). The data used includes electrical energy sales trends from 2016 to 2023 as well as external data from the Central Statistics Agency (BPS), which includes economic and demographic factors that affect energy demand, such as economic growth, population, and seasonal factors. LSTM was chosen for its ability to handle long-term dependencies in time series data, while SVR was used as a comparison to other regression methods. The resulting model is expected to provide more accurate predictions and be useful for PT PLN in planning the distribution of electrical energy efficiently. This research also contributes to the development of the application of machine learning in forecasting, which is growing in various sectors, including the energy sector, to improve operational efficiency and data-based decision making.

Financial time series prediction is remains a challenge, due to the nonstationary and fuzziness financial data. In this paper, we propose and achieve a hybrid financial time series model by combining the Maximum Entropy (ME), Support Vector Regression (SVR) and Trend model based on Artificial neural networks (ANNs) for forecasting financial time series. The method contains three steps. The first step is the feature processing and time alignment. And then, the results of the model analysis of the maximum entropy and support vector regression are taken as the input of the artificial neural network. At last, obtaining the final predicted results by BP algorithm. The global optimality of SVR model and the flexible feature selection make the input model better, which is favorable to get better forecasting results in proposed hybrid financial time series model based on neural networks. Experimental results clearly show that the accuracy of prediction for China closed-end fund net value can achieve 98.25%.

Financial time series prediction has always been a hot topic in the field of statistics learning. Aiming at the step selection problem of LSTM time series prediction model, this paper proposes an adaptive weighted LSTM model based on model average method. The model average is mainly reflected in two aspects: On the one hand, the proposed method takes intraday price information into account. Firstly, functional and nonlinear information of intraday price series are extracted through functional principal component analysis and kernel principal component analysis, and then Bagging is used to fit the residual sequence generated by the original LSTM model. On the other hand, the proposed method integrates the information of the model under different time Windows by using the weight based on distance correlation coefficient, and adaptively solves the step size selection problem, so as to improve the effectiveness of the overall model. The actual data analysis results show that the proposed method can effectively improve the prediction accuracy of the original LSTM model and has a certain robustness. Due to the flexibility of the proposed method, it can be used in time series prediction such as energy consumption prediction, environment detection and road traffic flow monitoring.

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.