A co-training approach for time series prediction with missing data

In this paper we consider the problem of missing data in time series analysis. We propose a semi-supervised co-training method to handle the problem of missing data. We transform the time series data to set of labeled and unlabeled data. Different predictors are used to predict the unlabelled data and the most confident labeled patterns are used to retrain the predictors further to and enhance the overall prediction accuracy. By labeling the unknown patterns the missing data is compensated for. Experiments were conducted on different time series data and with varying percentage of missing data using a uniform distribution. We used KNN base predictors and Fuzzy Inductive Reasoning (FIR) base predictors and compared their performance using different confidence measures. Results reveal the effectiveness of the co-training method to compensate for the missing values and to improve prediction. The FIR model together with the "similarity" confidence measures obtained in most cases the best results in our study.