I. Introduction
Time series can be found in many applications such as medical and biological diagnostics, analysis of financial time series, speech processing, or sensor analysis. Many steps in time series analysis, such as segmentation, representation, similarity measurement, etc. are often based on appropriate features extracted. From the time series, examples for features individual statistical measures (e.g., mean variance, number of zero crossings), features in the time domain (e.g., slope, curve), or spectral features (e.g., amplitudes attributed to certain frequency components of a time series). The run-time efficiency of algorithms for time series analysis is an important issue for two reasons:
The amount of data including time series data increases rapidly nowadays. The analysis of large time series data sets (i.e., big data in a time series context such as smart grid data) requires algorithms that fulfill harsh space and run-time constraints.
On-line algorithms (e.g., streaming algorithms) require a fast (often non-reversible) decision with strict runtime requirements (e.g., in the fields of ubiquitous or pervasive computing).