Welcome to Timeseria reference documentation!
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 tutorial for a more structured introduction. You can also run both interactivley in Binder.
Main modules and submodules
Provides base data structures: Points, Slots and Series with all their specializations. |
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Provides Units and TimeUnits (which fully support calendar arithmetic including DST changes). |
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Series transformations as slotting and resampling. |
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Data storages. |
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Provides time manipulation utilities, with a particular focus on proper timezone handling. |
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Provides base model classes. |
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Forecasting models. |
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Data reconstructions models. |
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Anomaly detection models. |
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Operations on the series, returning both scalar and other series. |
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Exceptions. |
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Plotting utilities. |
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Utility functions. |