Name
Abstract | ||
|---|---|---|
The rising capabilities of storing and registering data has increased the number of temporal datasets, boosting the attention on time series classification and forecasting. In case of multivariate time series, symbolic methods that try to predict phenomena transform the data into a more compact format to produce a representation of the time series easy to be handled in a machine learning framework... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TBDATA.2019.2918807 | IEEE Transactions on Big Data |
Keywords | DocType | Volume |
Hidden Markov models,Time series analysis,Forecasting,Histograms,Task analysis,Neural networks,Big Data | Journal | 7 |
Issue | ISSN | Citations |
5 | 2332-7790 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Paolo Soda | 1 | 407 | 39.44 |
| Rosa Sicilia | 2 | 7 | 4.38 |
| Ludovica Acciai | 3 | 0 | 0.34 |
| G. Iannello | 4 | 97 | 13.87 |