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Financial time series forecasting using twin support vector regression. 2019

Deepak Gupta, and Mahardhika Pratama, and Zhenyuan Ma, and Jun Li, and Mukesh Prasad
Department of Electronics and Computer Engineering, National Institute of Technology, Arunachal Pradesh, India.

Financial time series forecasting is a crucial measure for improving and making more robust financial decisions throughout the world. Noisy data and non-stationarity information are the two key factors in financial time series prediction. This paper proposes twin support vector regression for financial time series prediction to deal with noisy data and nonstationary information. Various interesting financial time series datasets across a wide range of industries, such as information technology, the stock market, the banking sector, and the oil and petroleum sector, are used for numerical experiments. Further, to test the accuracy of the prediction of the time series, the root mean squared error and the standard deviation are computed, which clearly indicate the usefulness and applicability of the proposed method. The twin support vector regression is computationally faster than other standard support vector regression on the given 44 datasets.

UI MeSH Term Description Entries
D007449 Investments Use for articles on the investing of funds for income or profit. Investment
D012044 Regression Analysis Procedures for finding the mathematical function which best describes the relationship between a dependent variable and one or more independent variables. In linear regression (see LINEAR MODELS) the relationship is constrained to be a straight line and LEAST-SQUARES ANALYSIS is used to determine the best fit. In logistic regression (see LOGISTIC MODELS) the dependent variable is qualitative rather than continuously variable and LIKELIHOOD FUNCTIONS are used to find the best relationship. In multiple regression, the dependent variable is considered to depend on more than a single independent variable. Regression Diagnostics,Statistical Regression,Analysis, Regression,Analyses, Regression,Diagnostics, Regression,Regression Analyses,Regression, Statistical,Regressions, Statistical,Statistical Regressions
D003658 Decision Making, Computer-Assisted Use of an interactive computer system designed to assist the physician or other health professional in choosing between certain relationships or variables for the purpose of making a diagnostic or therapeutic decision. Computer-Assisted Decision Making,Medical Decision Making, Computer-Assisted,Computer Assisted Decision Making,Decision Making, Computer Assisted,Medical Decision Making, Computer Assisted
D005376 Financial Management The obtaining and management of funds for institutional needs and responsibility for fiscal affairs. Endowments,Financial Activities,Funds,Activities, Financial,Activity, Financial,Endowment,Financial Activity,Fund,Management, Financial
D005544 Forecasting The prediction or projection of the nature of future problems or existing conditions based upon the extrapolation or interpretation of existing scientific data or by the application of scientific methodology. Futurology,Projections and Predictions,Future,Predictions and Projections
D006801 Humans Members of the species Homo sapiens. Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D013997 Time Factors Elements of limited time intervals, contributing to particular results or situations. Time Series,Factor, Time,Time Factor
D016208 Databases, Factual Extensive collections, reputedly complete, of facts and data garnered from material of a specialized subject area and made available for analysis and application. The collection can be automated by various contemporary methods for retrieval. The concept should be differentiated from DATABASES, BIBLIOGRAPHIC which is restricted to collections of bibliographic references. Databanks, Factual,Data Banks, Factual,Data Bases, Factual,Data Bank, Factual,Data Base, Factual,Databank, Factual,Database, Factual,Factual Data Bank,Factual Data Banks,Factual Data Base,Factual Data Bases,Factual Databank,Factual Databanks,Factual Database,Factual Databases
D060388 Support Vector Machine SUPERVISED MACHINE LEARNING algorithm which learns to assign labels to objects from a set of training examples. Examples are learning to recognize fraudulent credit card activity by examining hundreds or thousands of fraudulent and non-fraudulent credit card activity, or learning to make disease diagnosis or prognosis based on automatic classification of microarray gene expression profiles drawn from hundreds or thousands of samples. Support Vector Network,Machine, Support Vector,Machines, Support Vector,Network, Support Vector,Networks, Support Vector,Support Vector Machines,Support Vector Networks,Vector Machine, Support,Vector Machines, Support,Vector Network, Support,Vector Networks, Support
D018803 Models, Economic Statistical models of the production, distribution, and consumption of goods and services, as well as of financial considerations. For the application of statistics to the testing and quantifying of economic theories MODELS, ECONOMETRIC is available. Economic Models,Economic Model,Model, Economic

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