T-Time: Threshold-Based Data Mining on Time Series

Abstract

Abstract-Mining time series data is an important approach for the analysis in many application areas as diverse as biology, environmental research, medicine, or stock chart analysis. As nearly all data mining tasks on this kind of data depend on a distance function between two time series, a huge number of such functions has been developed during the last decades. The introduction of threshold-based distance functions presented a new concept of time series similarity and these functions were applied to data mining techniques on a wide spectrum of time series data. In this demonstration, we present the Java toolkit T-Time which is able to perform several data mining tasks for a complete range of threshold values in an interactive way. The results are visually presented in a very concise way so that the user can easily identify important threshold values. Combined with domain-specific knowledge, these pivotal values can yield novel insights beyond the means of the underlying data mining techniques the analysis is based on.

Cluster-Aware Compression with Provable K-means Preservation

“…Bagnall et al [11] proposed a binary clipping method for time series data, where the data are converted into 0 and 1 if they lie above or below the mean value baseline; this approach has been applied to speed up the execution of the K-means algorithm. Relevant is also the work of Aßfalg et al [12] who proposed threshold-based representations for querying and indexing time series data. Megalooikonomou et al [13] presented a piecewise vector-quantization approximation for time series data that preserves the shape of the original sequences with high accuracy.…”