Timeseria: An object-oriented time series processing library
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 l...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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| Series: | SoftwareX |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352711025000032 |
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| author | Stefano Alberto Russo Giuliano Taffoni Luca Bortolussi |
| author_facet | Stefano Alberto Russo Giuliano Taffoni Luca Bortolussi |
| author_sort | Stefano Alberto Russo |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-29c4585b5bfa4225a094291318b83b67 |
| institution | OA Journals |
| issn | 2352-7110 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | SoftwareX |
| spelling | doaj-art-29c4585b5bfa4225a094291318b83b672025-08-20T02:13:48ZengElsevierSoftwareX2352-71102025-02-012910203610.1016/j.softx.2025.102036Timeseria: An object-oriented time series processing libraryStefano Alberto Russo0Giuliano Taffoni1Luca Bortolussi2Italian National Center For HPC, Big Data and Quantum Computing, Bologna, Italy; INAF - Italian National Institute for Astrophysics - Observatory of Trieste, Italy; University of Trieste - Department of Mathematics, Informatics and Geosciences, Trieste, Italy; Correspondence to: Italian National Center For HPC, Big Data and Quantum Computing, Italy.Italian National Center For HPC, Big Data and Quantum Computing, Bologna, Italy; INAF - Italian National Institute for Astrophysics - Observatory of Trieste, ItalyUniversity of Trieste - Department of Mathematics, Informatics and Geosciences, Trieste, ItalyTimeseria 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.http://www.sciencedirect.com/science/article/pii/S2352711025000032PythonTime seriesData structuresForecastingReconstructionAnomaly detection |
| spellingShingle | Stefano Alberto Russo Giuliano Taffoni Luca Bortolussi Timeseria: An object-oriented time series processing library SoftwareX Python Time series Data structures Forecasting Reconstruction Anomaly detection |
| title | Timeseria: An object-oriented time series processing library |
| title_full | Timeseria: An object-oriented time series processing library |
| title_fullStr | Timeseria: An object-oriented time series processing library |
| title_full_unstemmed | Timeseria: An object-oriented time series processing library |
| title_short | Timeseria: An object-oriented time series processing library |
| title_sort | timeseria an object oriented time series processing library |
| topic | Python Time series Data structures Forecasting Reconstruction Anomaly detection |
| url | http://www.sciencedirect.com/science/article/pii/S2352711025000032 |
| work_keys_str_mv | AT stefanoalbertorusso timeseriaanobjectorientedtimeseriesprocessinglibrary AT giulianotaffoni timeseriaanobjectorientedtimeseriesprocessinglibrary AT lucabortolussi timeseriaanobjectorientedtimeseriesprocessinglibrary |