Summary of Timeseria: An Object-oriented Time Series Processing Library, by Stefano Alberto Russo et al.
Timeseria: an object-oriented time series processing library
by Stefano Alberto Russo, Giuliano Taffoni, Luca Bortolussi
First submitted to arxiv on: 12 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces Timeseria, an object-oriented Python library designed to simplify time series processing and enable the construction of statistical and machine learning models on top of it. Unlike traditional data analysis frameworks, Timeseria builds upon reusable logical units (objects) that can be combined to ensure consistency. This approach addresses common issues such as handling data losses, non-uniform sampling rates, and time zone differences. The library provides a range of base data structures, transformations for resampling and aggregation, manipulation operations, and extensible models for reconstruction, forecasting, and anomaly detection. Additionally, Timeseria integrates an interactive plotting engine capable of handling large datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Timeseria is a new way to work with time series data. It’s like a toolbox that makes it easy to handle big sets of numbers that change over time. The people who made Timeseria wanted to fix some common problems, like missing data or different ways of measuring things at different times. They also added features for doing things like predicting what will happen in the future and finding unusual patterns. This library is great because it lets you do all these things easily, and it even has a special tool for making pretty pictures of your data. |
Keywords
» Artificial intelligence » Anomaly detection » Machine learning » Time series
