1 Chapter 1 Pattern Recognition in Time Series

Massive amount of time series data are generated daily, in areas as diverse as astronomy, industry, sciences, and aerospace, to name just a few. One obvious problem of handling time series databases concerns with its typically massive size—gigabytes or even terabytes are common, with more and more databases reaching the petabyte scale. Most classic data mining algorithms do not perform or scale well on time series data. The intrinsic structural characteristics of time series data such as the high dimensionality and feature correlation, combined with the measurement-induced noises that beset real-world time series data, pose challenges that render classic data mining algorithms ineffective and inefficient for time series. As a result, time series data mining has attracted enormous amount of attention in the past two decades. In this chapter, we discuss the state-of-the-art techniques for time series pattern recognition, the process of mapping an input representation for an entity or relationship to an output category. Approaches to pattern recognition tasks vary by representation for the input data, similarity/distance measurement function, and pattern recognition technique. We will discuss the various pattern recognition techniques, representations, and similarity measures commonly used for time series. While the majority of work concentrated on univariate time series, we will also discuss the applications of some of the techniques on multivariate time series.