Name
Abstract | ||
|---|---|---|
In this paper, a model combining the wavelet transform and support vector machine to predict the time series is set up. First, wavelet transform is applied to decompose the series into sub series with different time scales. Then, the SVM is applied to the sub series to simulate and predict future behavior. And then by the inverse wavelet transform, the series are reconstructed, which is the prediction for the time series. The prediction precision of the new model is higher than that of the SVM model and the artificial neural network model for many processes, such as runoff, precipitation, temperature. The universal applicability of the new Wavelet-SVM model and the improvement direction are discussed in this paper. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-27503-6_85 | Communications in Computer and Information Science |
Keywords | Field | DocType |
Support vector machine,Wavelet Transformation,Regression | Harmonic wavelet transform,Pattern recognition,Lifting scheme,Computer science,Second-generation wavelet transform,Artificial intelligence,Discrete wavelet transform,Stationary wavelet transform,Wavelet packet decomposition,Wavelet transform,Wavelet | Conference |
Volume | Issue | ISSN |
243 | PART 1 | 1865-0929 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaohong Liu | 1 | 3 | 0.98 |
| Yanwei Zhu | 2 | 10 | 3.64 |
| Yongli Zhang | 3 | 0 | 0.34 |
| Xinchun Wang | 4 | 1 | 2.78 |