Approaches and Applications of Early Classification of Time Series: A Review

Early classification of time series has been extensively studied for minimizing class prediction delay in time-sensitive applications such as medical diagnostic and industrial process monitoring. A primary task of an early classification approach is to classify an incomplete time series as soon as possible with some desired level of accuracy. Recent years have witnessed several approaches for early classification of time series. As most approaches have solved the early classification problem using a diverse set of strategies, it becomes very important to make a thorough review of existing solutions. These solutions have demonstrated reasonable performance on a wide range of applications including human activity recognition, gene expression based health diagnostic, and industrial monitoring. In this article, we presenta-systematic review of the current literature on early classification approaches for both univariate and multivariate time series. We divide various existing approaches into four exclusive categories based on their proposed solution strategies. The four categories include prefix based, shapelet based, model based, and miscellaneous approaches. We discuss the applications of early classification and provide a quick summary of the current literature with future research directions. Impact Statement-Early classification is mainly an extension of classification with an ability to classify a time series using limited data points. It is true that one can achieve better accuracy if one waits for more data points, but opportunities for early interventions could equally be missed. In a pandemic situation such as COVID-19, early detection of an infected person becomes more desirable to curb the spread of the virus and possibly save lives. Early classification of gas (e.g., methyl isocyanate) leakage can help to avoid life-threatening consequences on human beings. Early classification techniques have been successfully applied to solve many time-critical problems related to medical diagnostic and industrial monitoring. This article provides a systematic review of the current literature on these early classification approaches for time series data, along with their potential applications. It also suggests some promising directions for further work in this area.