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. Time series represent the evolution of a phenomena over time, and their analysis is essential to capture the dynamics of the phenomena being studied, understand cause-and-effect relationships, and make predictions. However, a typical time series processing pipeline -- loading a data set, cleaning and plotting it, performing some operations, applying some models and inspecting the results -- still feels unnecessarily cumbersome. Scientists, engineers, analysts, and many other professional figures spend a considerable amount of time on repetitive procedures and on getting their code to work, instead of focusing on their core tasks.