A Semi-supervised Framework for Simultaneous Classification and Regression of Zero-Inflated Time Series Data with Application to Precipitation Prediction

Time series data with abundant number of zeros are common in many applications, including climate and ecological modeling, disease monitoring, manufacturing defect detection, and traffic accident monitoring. Classical regression models are inappropriate to handle data with such skewed distribution because they tend to underestimate the frequency of zeros and the magnitude of non-zero values in the data. This paper presents a hybrid framework that simultaneously perform classification and regression to accurately predict future values of a zero-inflated time series. A classifier is initially used to determine whether the value at a given time step is zero while a regression model is invoked to estimate its magnitude only if the predicted value has been classified as nonzero. The proposed framework is extended to a semi-supervised learning setting via graph regularization. The effectiveness of the framework is demonstrated via its application to the precipitation prediction problem for climate impact assessment studies.