The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
The technology of phase space construction and Support Vector Machines(SVM) is introduced firstly. Then a novel complex time series forecasting approach based on SVM is proposed. The complex time series is decomposed into long-term trend series and short-term fluctuation series. The SVM regressive forecasting model is constructed respectively. The proposed forecasting approach is applied to the Shanghai...
Forecasting applications on the stock market attract much interest from researchers in the artificial intelligence field. The problem tackled in this study concerns predicting the direction of change of stock price indices, formulated in terms of binary classification. We use gene expression programming to evolve pools of binary classifiers and investigate several approaches to construct ensembles...
Due to the fluctuation and complexity of the financial time series, it is difficult to use any single artificial technique to capture its non-stationary property and accurately describe its moving tendency. So a novel hybrid intelligent forecasting model based on empirical mode decomposition (EMD) and support vector regression (SVR) is proposed. EMD can adaptively decompose the complicated raw data...
The stock market is considered as a high complex and dynamic system. Many machine learning and data mining technologies are used for stock analysis, but it still leaves an open question about how to integrate these methods with the plentiful knowledge and techniques accumulated in stock investment which are critical to the successful stock analysis. In this paper, we propose an intelligent stock trading...
An admissible manifold wavelet kernel is proposed to construct manifold wavelet support vector machine (MWSVM) for forecasting stock returns. The manifold wavelet kernel is obtained by incorporating manifold theory into wavelet technique in support vector machine (SVM). Since manifold wavelet function can yield features that describe of the stock time series both at various locations and at varying...
A novel forecasting model of foreign exchange market based on least squares support vector machine (LS-SVM) is proposed in this paper. The experiment on the prediction of four kinds of daily exchange rate recorded is carried out. Grid search method is used to determine the LS-SVM parameters automatically in the forecasting process. The results show the precision of fitting and forecasting are very...
Global prediction techniques such as support vector machines show accurate prediction for time series data; however, such models tend to delay the predicted output. Fuzzy systems have benefits in local optimum, thus producing significant results within training sets. Unfortunately, the existing techniques sometimes give undesired effects of surface oscillation at predicted outputs. This paper presents...
In the analysis of predicting share price based on least squares support vector machine (LS-SVM), the instability of the time series could lead to decrease of prediction accuracy. On the other hand, two SVM parameters, c and sigma, must be carefully predetermined in establishing an efficient LS-SVM model. In order to solve the problems mentioned above, in this paper, the hybrid of wavelet transform...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.