Welcome to Timeseria reference documentation!

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 numerical and data analysis frameworks, Timeseria does not make use of low level data structures as arrays and matrices to represent time series data. Instead, it builds up from well defined and reusable logical units (objects), which can be easily combined together in order to ensure an 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.

This is the reference documentations. To get started more gently, you can have a look at the quickstart or welcome notebooks. Usage examples are provided in the Timeseria-notebooks repository, and a Docker image ready to be played with is available on Docker Hub.


Main modules and submodules

units

Units, including the TimeUnit, which fully supports calendar arithmetic.

datastructures

Base data structures as Points, Slots, and Series.

interpolators

Interpolators.

operations

Operations on the series.

transformations

Series transformations as resampling and aggregation.

models.base

Base model classes.

models.forecasters

Forecasting models.

models.reconstructors

Data reconstructions models.

models.anomaly_detectors

Anomaly detection models.

models.calibrators

Calibration models.

storages

Data storages, as the CSV file storage.

plots

Plotting engines.

exceptions

Exceptions.

utils

Utility functions.

logger

Library logging setup.

Other resources