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 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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Publication Details

Journal
SoftwareX
Published
2025-01-27
DOI
https://doi.org/10.1016/j.softx.2025.102036
Primary Topic
Time Series Analysis and Forecasting
Type
article
Field-Weighted Citation Impact
0.00
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article

Timeseria: An object-oriented time series processing library

Giuliano Taffoni, Stefano Alberto Russo, Luca Bortolussi
SoftwareX
Time Series Analysis and Forecasting
article

Timeseria: An object-oriented time series processing library

Giuliano Taffoni, Stefano Alberto Russo, Luca Bortolussi
article en

Abstract

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.

SoftwareXVol. 29
University of Trieste (IT), Società Italiana di Fisica (IT), National Institute for Astrophysics (IT)
Openalex Percentile: Top 99%
Time Series Analysis and Forecasting
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