Timeseria: an object-oriented time series processing library
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 7 of its 19 references that are in the map. First posted on arXiv 12 Oct 2024.
Builds on
7 of 19 references.
References · 7
- 2019PyOD: A Python Toolbox for Scalable Outlier Detectioncited by 221
- 2020A review on outlier/anomaly detection in time series datacited by 187
- 2020Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progresscited by 148
- 2021Darts: User-Friendly Modern Machine Learning for Time Seriescited by 90
- 2022Sintel: A Machine Learning Framework to Extract Insights from Signalscited by 16
- 2021tsflex: flexible time series processing & feature extractioncited by 8
- 2022Greykite: Deploying Flexible Forecasting at Scale at LinkedIncited by 2
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