Zubin Abraham and Pang-Ning Tan
June, 2009
Many time series forecasting problems involve skewed time series, where many of the real-valued observations are zeros. Due to the skewed distribution, current regression models tend to underestimate the future prediction values. To overcome this problem, we present a novel semi-supervised learning framework that simultaneously combines a classification model (to predict whether the observation value is exactly zero) and a regression model (to predict the actual value of the non-zero observation). We demonstrate the effectiveness of the framework in terms of its application to precipitation prediction for climate modeling.
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