专为复杂时间序列设计的可组合数据处理库，解决采样不均、时区等问题。
Timeseria: an object-oriented time series processing library
- 基于对象化设计，通过可复用单元组合处理时间序列
- 原生支持非均匀采样、时区转换、数据丢失等现实问题
- 适合需要高一致性的时间序列分析与建模场景
Timeseria 是一个用 Python 实现的面向对象时间序列处理库，旨在简化时间序列数据操作并构建统计与机器学习模型。不同于常规数据分析框架，它从明确定义且可复用的逻辑单元（对象）出发，可轻松组合以保证高度一致性。该设计天然解决诸多常被忽视的复杂问题，如数据丢失、非均匀采样率、聚合数据与瞬时观测差异、时区与夏令时等。库内提供完整的基础数据结构、重采样与聚合转换、通用数据操作，以及可扩展的数据重建、预测与异常检测模型。同时集成全功能交互式绘图引擎，可高效处理数百万数据点。
原文摘要 · Abstract (English)
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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