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

Richard James Povinelli, Marquette University

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.

Recommended Citation

Richard James Povinelli, "Time series data mining: Identifying temporal patterns for characterization and prediction of time series events" (January 1, 1999). Dissertations (1962 - 2010) Access via Proquest Digital Dissertations. Paper AAI9953495.
http://epublications.marquette.edu/dissertations/AAI9953495

Share

COinS