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Capaldi is a Python-and-R-based time series expert system. It's designed to take a generic time series - that is, floating point or integer values at a sequence of times, and return:

• A list of candidate models fit to the data.
• Some basic information about why these fitted models are suitable
• Some basic information about why other candidate models can't be reasonably fit.

This information will be returned as a mix of text and graphs, packed into a JSON Object.

Currently, Capaldi is set up to take a generic time series, regroup the data into a number of different date/time buckets, and run a few algorithms in bulk on it. It provides some minimal sanity checking based on the number of buckets produced and the some of the properties of the data.

It is not yet set up to provide the planned expert feedback, plain text responses, or pretty graphics.

Capaldi is implemented as a Python module, defined in the capaldi directory. With the repository, the algs directory includes submodules for each individual time series algorithm. Many of these submodules are wrappers for R packages that must be hosted on a partner OpenCPU instance; OpenCPU will serve a local installation of R and make it accessible via a JSON API.

Sanity checks are currently hosted in algs as well but will be getting moved to a new checks module. Many of the algorithms depend on the same assumptions so these can be collapsed to simple checklist.

The principal function -capaldi() in capaldi/capaldi.py takes a data frame, divides it into a number of chunks, tests each independence assumption, and then runs each algorithm on each chunk.