Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Approximate clustering of time series using compact model-based descriptions
DASFAA'08 Proceedings of the 13th international conference on Database systems for advanced applications
A novel bit level time series representation with implication of similarity search and clustering
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
| Hi-index | 0.00 |
Recently, we have proposed a novel method for the compression of time series based on mathematical models that explore dependencies between different time series. This representation models each time series by a combination of a set of specific reference time series. The cost of this representation depend only on the number of reference time series rather than on the length of the time series. In this demonstration, we present a Java toolkit which is able to perform several data mining tasks based on this novel time series representation. In particular, this framework allows the user to explore the properties of our novel approach in comparison to other state-of-the-art compression methods. The results are visually presented in a very concise way so that the user can easily identify important settings of the model-based time series representation.