Abstract

Wavelet packet theory and support vector regression (SVR) were introduced into server load prediction. A novel prediction algorithm called wavelet packet-SVR was proposed. Firstly, the algorithm decomposed and reconstructed the load time series into several signal branches by wavelet packet analysis. Secondly, SVR prediction models were constructed respectively to these branches and finally their predicted results were combined into final load value. Theory analysis and experiments show that wavelet packet transform is the extension of wavelet theory and has better frequency resolution. So it can decompose the original load series into several time series that have simpler frequency components and are easier to be forecasted; support vector regression has greater generation ability and guarantees global minima for given training data, it performs well for non-stationary time series prediction. So the proposed method is superior to wavelet based approach

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.