Mathematical Problems in Engineering
Volume 2010 (2010), Article ID 513810, 14 pages
Research Article

Incomplete Time Series Prediction Using Max-Margin Classification of Data with Absent Features

1College of Computer Science, University of Chongqing, Chongqing 400030, China
2School of Mechatronic Engineering, Northwestern Polytechnical University, Xi'an 710072, China

Received 18 February 2010; Revised 24 March 2010; Accepted 20 April 2010

Academic Editor: Ming Li

Copyright © 2010 Shang Zhaowei et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper discusses the prediction of time series with missing data. A novel forecast model is proposed based on max-margin classification of data with absent features. The issue of modeling incomplete time series is considered as classification of data with absent features. We employ the optimal hyperplane of classification to predict the future values. Compared with traditional predicting process of incomplete time series, our method solves the problem directly rather than fills the missing data in advance. In addition, we introduce an imputation method to estimate the missing data in the history series. Experimental results validate the effectiveness of our model in both prediction and imputation.