Incomplete electrocardiogram time series prediction

Abstract

The prevalence of Big Data has led to the operation practice based on time series data from multiple sources in many practical applications. The prediction analysis of time series, a fundamental objective of time series data crunch, is an integral part for planning and decision making. However, the prediction analysis based on raw time series data is hardly satisfactory as missing values are usually involved in raw data samples, even if a collection of the existing regression models are available to handle the complete time series prediction problems. To achieve higher prediction precision with incomplete raw time series data, in this paper, we describe a new framework called ISM (Incomplete time series prediction based on Selective tensor modeling and Multi-kernel learning). ISM is composed of three steps. First, multi-source time series are fused and then a selective tensor is constructed from K most relevant raw data sets. Second, the selective tensor is further factorized by ISM with the sparsity constraint to extract the common latent factors across multiple sources. Finally, these factors serve as the training features of multi-kernel learning, which is an effective approach to build multi-source regression models. Extensive experiments on the electrocardiogram data set demonstrate that the proposed framework ISM achieves a superior performance of time series prediction with missing data.