Contemporary Approaches to Analyze Non-stationary Time-series: Some Solutions and Challenges

Abstract

Enhancement of technology yields more complex time-dependent outcomes for better understanding and analysis. These outcomes are generating more complex, unstable, and high-dimensional data from non-stationary environments. Hence, more challenges are arising day by day to fulfill the increasing demand for future estimation. Thus, in this paper, an extensive study has been presented to comprehend the statistical complexity and randomness of non-stationary time series (NS-TS) data at the atomic level. This survey briefly explains the basic principles and terms related to non-stationary time series (NS-TS). After understanding the fundamentals of NS-TS, this survey categorized non-stationarity into groups and their subgroups based on a change in statistical behavior. It is followed by a comprehensive discussion on contemporary approaches proposed by researchers in each category of non-stationarity. These algorithms include clustering, classification, and regression techniques to deal with different types of domains. Every category of non-stationarity consists of a separate table to draw some advantages and disadvantages of existing approaches. At the end of each non-stationarity type, a short discussion and critical analysis have been done. In the conclusion section, it observed that this research sphere still has so many open challenges that need to be addressed and demand more exploration. Furthermore, it discusses the possible solution of improvisation in future research.