Name
Abstract | ||
|---|---|---|
Linear and nonlinear models for time series analysis and prediction are well-established. Clustering methods have also been applied to this area. This paper explores a framework that can be used to cluster time series data. The range of values of a time series is clustered. Then the time series is clustered by data windows that flow into the initial set of value clusters. This allows predictive temporal patterns to be discovered across the whole range of values. |
Year | Venue | Keywords |
|---|---|---|
2015 | 2015 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) | Adaptive Resonance Theory, Time Series Prediction, Clustering |
Field | DocType | ISSN |
Cluster (physics),Time series,Nonlinear system,Wind speed,Correlation clustering,Pattern recognition,Computer science,Artificial intelligence,Cluster analysis,Machine learning | Conference | 2161-4393 |
Citations | PageRank | References |
0 | 0.34 | 8 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Clayton Smith | 1 | 1 | 0.70 |
| Wunsch II Donald C. | 2 | 1354 | 91.73 |