| Autor: |
Azim Ahmadzadeh, Kankana Sinha, Berkay Aydin, Rafal A. Angryk |
| Jazyk: |
angličtina |
| Rok vydání: |
2020 |
| Předmět: |
|
| Zdroj: |
SoftwareX, Vol 12, Iss , Pp 100518- (2020) |
| Druh dokumentu: |
article |
| ISSN: |
2352-7110 |
| DOI: |
10.1016/j.softx.2020.100518 |
| Popis: |
We developed a domain-independent Python package to facilitate the preprocessing routines required in preparation of any multi-class, multivariate time series data. It provides a comprehensive set of 48 statistical features for extracting the important characteristics of time series. The feature extraction process is automated in a sequential and parallel fashion, and is supplemented with an extensive summary report about the data. Using other modules, different data normalization methods and imputation are at users’ disposal. To cater the class-imbalance issue, that is often intrinsic to real-world datasets, a set of generic but user-friendly, sampling methods are also developed. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
