MVTS-Data Toolkit: A Python package for preprocessing multivariate time series data
| Autor: | Berkay Aydin, Kankana Sinha, Rafal A. Angryk, Azim Ahmadzadeh |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Multivariate statistics
Class imbalance Computer science Feature extraction Data analysis computer.software_genre 01 natural sciences Multi-class Database normalization 03 medical and health sciences 0103 physical sciences Preprocessor Imputation (statistics) Time series 010306 general physics Sampling Multivariate time series 030304 developmental biology computer.programming_language lcsh:Computer software 0303 health sciences Sampling (statistics) Python (programming language) Computer Science Applications lcsh:QA76.75-76.765 Data mining computer Software |
| Zdroj: | SoftwareX, Vol 12, Iss, Pp 100518-(2020) |
| ISSN: | 2352-7110 |
| 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: | OpenAIRE |
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