Editorial: Advanced Time Series Analysis in Geosciences

Abstract

A time series is an ordered sequence of data indexed by time. In other words, it is a sequence of discrete-time data, usually obtained at equally spaced points in time. Time series analysis is the attempt of extracting meaningful characteristics and statistical information from data organized in chronological order. Nowadays, there are numerous types of data analysis approaches available for time series which are suitable for different purposes: diagnosing past behavior, prediction and forecasting, curve fitting, interpolation and extrapolation, classification and clustering, segmentation and decomposition, frequency characterization, etc. The theoretical advances in time series analysis started early at the beginning of the last century with new developments in the field of stochastic processes. The first actual application of autoregressive models to time series can be identified in the work of Yule (1927) and Walker (1931). But it is since the pioneering book “Time Series Analysis” by Box and Jenkins in the 1970s (Box and Jenkins, 1970), that many lines of study in time series analysis have been developed. Today we are witnessing a rapid increase in quantity, quality and importance of time series data in Earth Sciences. Across its vast number of subdisciplines, the massive production of data, e.g., through the growth of continuous monitoring networks and the availability of abundant remote sensing data, is making increasingly important the use of analysis tools capable of synthesizing information contained in large time series. To deal with the increasing amount of available data in an automated way, the first emerging approaches of machine learning in time series analysis date back to the early 1980s (Nielsen, 2019). At present, although classical methods are still dominant, machine learning is rapidly emerging as a valid alternative approach to time series analysis, finding effectiveness especially in multivariate time series. It is clear to everyone that, as continuous monitoring and data gathering become even more common in geosciences, the need for powerful time series analysis techniques, either classical/ statistical or machine learning based techniques, will further increase. The impact of this need is proven by an exponential growth shown by the occurrence of the keyword “time series analysis” in papers published from 1985 to 2020, as indicated by both WoS (https://webofknowledge.com/) and Scopus (https://www.scopus.com/) databases (Figure 1). In this context, this Research Topic collects some illustrative examples of state of the art research from across the world to delineate the dramatic and diverse nature of time series analysis in geosciences. With the analysis of data over time providing the basis of many modern scientific disciplines, this research covers a variety of applications in the field of geosciences.