Data Mining in Time Series: Current Study and Future Trend

Abstract

Time series represent sequences of data points where usually their order is defined by the time when they were recorded. Thus, virtually any sequential recordings can be stored as time series: Stock prices, weather conditions, physical system parameters change, product quality monitoring, etc. This leads to ubiquity of time series in all scientific and practical fields (Esling and Agon, 2012); hence, they have attracted significant research efforts over the past decades. The time series can be univariate, i.e., only one variable recorded, or multivariate, i.e., a set of observations from different sources recorded at some time points. The tasks of time series analysis essentially defined to extract meaningful information from the collections of data points or to organize fast and easy access to the necessary data. In this article, we will briefly discuss the major tasks such as classification, clustering, prediction, segmentation and indexing of time series.