Title :
A Novel Time Streams Prediction Approach Based on Exponential Smoothing
Author_Institution :
Sch. of Comput. Sci. & Technol., China Univ. of Min. Technol., Xuzhou, China
Abstract :
A data stream prediction algorithm using Linear Regression based on Exponential Smoothing method was proposed in this paper, namely Exponential Smoothing based Linear Regression Analysis (ES_LRA) data stream prediction algorithm. The ES_LRA algorithm only processes the current sliding window, which can improve the operation efficiency; In the meantime, it applied a Smoothing Coefficient(α) through the exponential smoothing method to smooth the estimate parameter in order to eliminate the concussion caused by unit data and increase the prediction accuracy. The experiment simulated a building fire by FDS4.0 and estimated the trend of the fume temperature. Analytical and experimental evidence show that the ES_LRA algorithm performs better both on prediction accuracy and operate efficiency.
Keywords :
media streaming; regression analysis; smoothing methods; ES_LRA; data stream prediction algorithm; exponential smoothing; exponential smoothing based linear regression analysis; novel time streams prediction approach; sliding window; smoothing coefficient; Accuracy; Algorithm design and analysis; Data analysis; Fires; Linear regression; Parameter estimation; Prediction algorithms; Smoothing methods; Temperature; Windows;
Conference_Titel :
Multimedia and Information Technology (MMIT), 2010 Second International Conference on
Conference_Location :
Kaifeng
Print_ISBN :
978-0-7695-4008-5
Electronic_ISBN :
978-1-4244-6602-3
DOI :
10.1109/MMIT.2010.107