Timeseria: An object-oriented time series processing library

被引:0
|
作者
Russo, Stefano Alberto [1 ,2 ,3 ]
Taffoni, Giuliano [1 ,2 ]
Bortolussi, Luca [3 ]
机构
[1] Italian Natl Ctr HPC, Big Data & Quantum Comp, Bologna, Italy
[2] INAF Italian Natl Inst Astrophys Observ Trieste, Trieste, Italy
[3] Univ Trieste, Dept Math Informat & Geosci, Trieste, Italy
关键词
!text type='Python']Python[!/text; Time series; Data structures; Forecasting; Reconstruction; Anomaly detection;
D O I
10.1016/j.softx.2025.102036
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
Timeseria is an object-oriented time series processing library implemented in Python, which aims at making it easier to manipulate time series data and to build statistical and machine learning models on top of it. Unlike common data analysis frameworks, it builds up from well defined and reusable logical units (objects), which can be easily combined together in order to ensure a high level of consistency. Thanks to this approach, Timeseria can address by design several non-trivial issues which are often underestimated, such as handling data losses, non-uniform sampling rates, differences between aggregated data and punctual observations, time zones, daylight saving times, and more. Timeseria comes with a comprehensive set of base data structures, data transformations for resampling and aggregation, common data manipulation operations, and extensible models for data reconstruction, forecasting and anomaly detection. It also integrates a fully featured, interactive plotting engine capable of handling even millions of data points.
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页数:8
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