Time series data mining: identifying temporal patterns for characterization and prediction of time series events

TLDR

Traditional time series analysis requires stationarity, normality, and independent residuals, limiting its ability to capture complex nonperiodic, nonlinear, irregular, or chaotic characteristics. The authors introduce Time Series Data Mining (TSDM), a new framework for analyzing time series data. TSDM adapts data‑mining concepts to time series, providing methods that uncover hidden temporal patterns predictive of events, grounded in a theoretical review and applied to engineering and financial data. TSDM methods overcome the limitations of traditional techniques.

Abstract

A new framework for analyzing time series data called Time Series Data Mining (TSDM) is introduced. This framework adapts and innovates data mining concepts to analyzing time series data. In particular, it creates a set of methods that reveal hidden temporal patterns that are characteristic and predictive of time series events. Traditional time series analysis methods are limited by the requirement of stationarity of the time series and normality and independence of the residuals. Because they attempt to characterize and predict all time series observations, traditional time series analysis methods are unable to identify complex (nonperiodic, nonlinear, irregular, and chaotic) characteristics. TSDM methods overcome limitations of traditional time series analysis techniques. A brief historical review of related fields, including a discussion of the theoretical underpinnings for the TSDM framework, is made. The TSDM framework, concepts, and methods are explained in detail and applied to real-world time series from the engineering and financial domains.