Timeseria: an object-oriented time series processing library

TL;DR

Timeseria is a Python library that simplifies time series data manipulation and modeling through an object-oriented approach, addressing complex issues like data gaps, sampling, and time zones with extensive features.

Contribution

It introduces an object-oriented framework for time series processing that enhances consistency and flexibility, with built-in solutions for common challenges and extensible modeling capabilities.

Findings

Addresses data loss and non-uniform sampling issues.

Provides efficient handling of large datasets with interactive plotting.

Enables building robust statistical and machine learning models.

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.