%0 Conference Paper %1 4024193 %A Feng, Huifang %A Shu, Yantai %A Wang, Shuyi %A Ma, Maode %B Communications, 2006. ICC '06. IEEE International Conference on %D 2006 %K predicting svm timeseries wlan %P 597-602 %R 10.1109/ICC.2006.254860 %T SVM-Based Models for Predicting WLAN Traffic %U http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=4024193&abstractAccess=no&userType=inst %V 2 %X A novel type of learning machine called support vector machine (SVM) has been receiving increasing interests in the areas ranging from its original application in pattern recognition to other applications such as regression estimation due to its remarkable generalization performance. In this paper, we employ the SVM to forecast traffic in WLANs. We study the issues of one-step-ahead prediction and multi-step-ahead prediction without any assumption on the statistical property of actual WLAN traffic. We also evaluate the performance of different prediction models using four real WLAN traffic traces. The simulation results will show that among these methods, SVM outperforms other prediction models in WLAN traffic forecasting for both one-step-ahead and multi-step-ahead prediction.

@inproceedings{4024193, abstract = {A novel type of learning machine called support vector machine (SVM) has been receiving increasing interests in the areas ranging from its original application in pattern recognition to other applications such as regression estimation due to its remarkable generalization performance. In this paper, we employ the SVM to forecast traffic in WLANs. We study the issues of one-step-ahead prediction and multi-step-ahead prediction without any assumption on the statistical property of actual WLAN traffic. We also evaluate the performance of different prediction models using four real WLAN traffic traces. The simulation results will show that among these methods, SVM outperforms other prediction models in WLAN traffic forecasting for both one-step-ahead and multi-step-ahead prediction.}, added-at = {2013-07-01T18:38:06.000+0200}, author = {Feng, Huifang and Shu, Yantai and Wang, Shuyi and Ma, Maode}, biburl = {https://www.bibsonomy.org/bibtex/2fbf058dd18f9e8dff8176933a90a1321/nosebrain}, booktitle = {Communications, 2006. ICC '06. IEEE International Conference on}, description = {IEEE Xplore - SVM-Based Models for Predicting WLAN Traffic}, doi = {10.1109/ICC.2006.254860}, interhash = {94b8aeb1aec9f7effa73743c6509c71b}, intrahash = {fbf058dd18f9e8dff8176933a90a1321}, issn = {8164-9547}, keywords = {predicting svm timeseries wlan}, pages = {597-602}, timestamp = {2013-07-01T18:38:28.000+0200}, title = {SVM-Based Models for Predicting WLAN Traffic}, url = {http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=4024193&abstractAccess=no&userType=inst}, volume = 2, year = 2006 }