ELKI in Time: ELKI 0.2 for the Performance Evaluation of Distance Measures for Time Series
SSTD '09 Proceedings of the 11th International Symposium on Advances in Spatial and Temporal Databases
Efficient algorithm for a novel pattern of time series
Expert Systems with Applications: An International Journal
A review on time series data mining
Engineering Applications of Artificial Intelligence
| Hi-index | 0.00 |
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.