Predicting Missing Parts in Time Series Using Uncertainty Theory

As extremely large time series data sets grow more prevalent in a wide variety of applications, including biomedical data analysis, diagnosis and monitoring systems and exploratory data analysis in scientific and business time series, the need of developing efficient analysis methods is high. However, essential preprocessing algorithms are required in order to obtain positive results. The goal of this paper is to propose a novel algorithm that is appropriate for filling missing parts of time series. This algorithm, named FiTS (Filling Time Series), was evaluated over 11 congestive heart failure patients’ ECGs (Electrocardiogram). Those patients using electronic microdevices with which were recording their ECGs and sending them via telephone to a home care monitoring system, over a period of 8 to 16 months. Randomly missing parts in each ECG were introduced in the initial ECG. As a result, FiTS had 100% of successfully completion with high reconstructed signal accuracy.