Name
Abstract | ||
|---|---|---|
Knowledge Discovery in time series usually requires symbolic time series. Many discretization methods that convert numeric time series to symbolic time series ignore the temporal order of values. This often leads to symbols that do not correspond to states of the process generating the time series and cannot be interpreted meaningfully. We propose a new method for meaningful unsupervised discretization of numeric time series called Persist. The algorithm is based on the Kullback-Leibler divergence between the marginal and the self-transition probability distributions of the discretization symbols. Its performance is evaluated on both artificial and real life data in comparison to the most common discretization methods. Persist achieves significantly higher accuracy than existing static methods and is robust against noise. It also outperforms Hidden Markov Models for all but very simple cases. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1145/1081870.1081953 | KDD |
Keywords | Field | DocType |
hidden markov models,discretization method,symbolic time series,time series,knowledge discovery,kullback-leibler divergence,numeric time series,optimizing time series discretization,common discretization method,meaningful unsupervised discretization,discretization symbol,kullback leibler divergence,transition probability,persistence,discretization,hidden markov model | Data mining,Discretization,Order of integration,Discretization error,Computer science,Probability distribution,Knowledge extraction,Artificial intelligence,Hidden Markov model,Machine learning,Discretization of continuous features | Conference |
ISBN | Citations | PageRank |
1-59593-135-X | 45 | 1.82 |
References | Authors | |
13 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fabian Mörchen | 1 | 372 | 17.94 |
| Alfred Ultsch | 2 | 403 | 51.77 |