Using support vector machines for time series prediction

By U. Thissen, R. V. Brakel, A. P. Weijer, Willem J. Melssen, Lutgarde M. C. Buydens. 2003

398 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/f3e8ea03aa6555733f54bd9c8907ec982bac7760

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Junyi Zhou
2. Jing Shi
3. Gong Li
4. A. Taşcıkaraoǧlu
5. Qinghua Hu
6. Daniel Ambach
7. Florian Ziel
8. Jaesung Jung

Citations in the corpus

• Fine tuning support vector machines for short-term wind speed forecasting (2011, background): “Capable of modeling nonlinear relations in an efficient and stable way, SVM has demonstrated good performance on regression and time-series prediction although the training time might be long for large datasets [24].”