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

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.

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