Timeseria is a data processing library which aims at making it easy to handle time series data and to build statistical and machine learning models on top of it.
It provides a built-in set of common operations (resampling, slotting, differencing etc.) as well as models (reconstruction, forecasting and anomaly detection), and both custom operations and models can be easily plugged in.
Timeseria also tries to address by design all those annoying things which are often left as an implementation detail but that actually cause wasting massive amounts of time - as handling data losses, non-uniform sampling rates, differences between time-slotted data and punctual observations, variable time units, timezones, DST changes and so on.
This is the refeerence documentations, and it is quite essential. To get started more gently, you can have a look at the quickstart or at the welcome notebooks.
Examples are provided in the Timeseria-notebooks repository, and a Docker image ready to be played with is available on Docker Hub.
.. automodule:: timeseria
:members:
:inherited-members:
:undoc-members:
.. autosummary::
:toctree:
datastructures
units
transformations
storages
time
models.base
models.forecasters
models.reconstructors
models.anomaly_detectors
operations
exceptions
plots
utilities