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https://dl.acm.org/citation.cfm?id=1293997
This paper proposes a modified version of support vector machines (SVMs), called dynamic support vector machines (DSVMs), to model non-stationary time series. The DSVMs are obtained by incorporating the problem domain knowledge -- non-stationarity of time series into SVMs.Cited by: 90
https://www.researchgate.net/publication/220571317_Dynamic_support_vector_machines_for_non-stationary_time_series_forecasting
Download Citation Dynamic support vector machines for non-stationary time series forecasting This paper proposes a modified version of support vector machines (SVMs), called dynamic support ...
https://content.iospress.com/articles/intelligent-data-analysis/ida00079
Jul 25, 2001 · This paper proposes a modified version of support vector machines (SVMs), called dynamic support vector machines (DSVMs), to model non-stationary time series. The DSVMs are obtained by incorporating the problem domain knowledge -- non-stationarity ofCited by: 90
http://trace.tennessee.edu/cgi/viewcontent.cgi?article=3107&context=utk_gradthes
financial time series forecasting is often tagged as the most challenging application of time series forecasting. In this study, a novel approach known as Support Vector Regression (SVR) for forecasting non-stationary time series was adopted and the feasibility of applying this method to five financial time series was examined.
https://www.deepdyve.com/lp/ios-press/dynamic-support-vector-machines-for-non-stationary-time-series-a1OyGpED80
Jan 01, 2002 · Read "Dynamic support vector machines for non-stationary time series forecasting, Intelligent Data Analysis" on DeepDyve, the largest online rental service for scholarly research with thousands of academic publications available at your fingertips.
https://www.sciencedirect.com/science/article/pii/S0895717705004760
Cao and Gu proposed a dynamic SVM model (DSVM) to solve non-stationary time series problems. Their experimental results demonstrated that DSVMs outperform standard SVMs in forecasting non-stationary time series. Tay and Cao presented a C-ascending SVM to model non-stationary financial time series. Their experimental results indicated that C ...Cited by: 139
https://www.researchgate.net/publication/225454618_-Descending_Support_Vector_Machines_for_Financial_Time_Series_Forecasting
This paper proposes a modified version of support vector machines (SVMs), called ε-descending support vector machines (ε-DSVMs), to model non-stationary financial time series.
http://www.sapub.org/global/showpaperpdf.aspx?doi=10.5923/j.statistics.20140401.03
Using Support Vector Machines in Financial Time Series Forecasting Mahmoud K. Okasha Department of Applied Statistics, Al-Azhar University – Gaza, Palestine Abstract Forecasting financial time series, such as stock price indices, is a complex process. This is because financial time series are usually quite noisy and involve ambiguous seasonal ...Cited by: 6
https://stackoverflow.com/questions/49081801/time-series-forecasting-using-support-vector-machine-svm-in-r
That's it. However, support vector machine is not commonly regarded as the best method for time series forecasting, especially for long series of data. It can perform good for few observations ahead, but I wouldn't expect good results for forecasting eg. daily data …
https://www.sciencedirect.com/science/article/pii/S0925231203003722
For the non-separable case, ... F. Girosi, Nonlinear prediction of chaotic time series using support vector machines, in: Proceedings of the IEEE Workshop on Neural Networks for Signal Processing, Amelia Island, FL, 1997, pp. 511–520. ... L. CaoApplication of support vector machines in financial time series forecasting. Omega, 29 (2001), pp ...Cited by: 1474
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