This is work in progress.

This project uses rpy2 to expose most of the functionality of R's Forecast package in python. Some related functions in the stats and base packages are also exposed (e.g. seasonal decompositions). A few less-commnonly used functions and arguments are not exposed.

An example of generating a forecast:

from rforecast import wrappers
from rforecast import ts_io

stock = ts_io.read_series('data/livestock.csv')
fc = wrappers.forecast(stock)
print fc

This example uses the livestock series in data/ under the installation directory.

An example of generating an STL decomposition:

aus = ts_io.read_series('aus.csv')
dc = wrappers.stl(aus, s_window=5)
print dc

The rforecast.py package uses Pandas Series objects to represent time series. For seasonal series, it uses a multindex with the second level of the index denoting the season. The read_series function in ts_io will return a series with the index constructed correctly. If the data are already in a Python sequence, such as a list or numpy array, you can convert it to a series of the right form like this:

from rforecast import converters
# A slice of the 'oil' data from R package fpp, available in data/
data =  [509, 506, 340, 240, 219, 172, 252, 221, 276, 271, 342, 428, 442, 432, 437]
ts = converters.sequence_as_series(data, start=1980)
print ts

# A seasonal (quarterly) series:
data = [30.05, 19.14, 25.31, 27.59, 32.07, 23.48, 28.47, 35.12,
        36.83, 25.00, 30.72, 28.69, 36.64, 23.82, 29.31, 31.77]
ts = converters.sequence_as_series(data, start=(1991, 1), freq=4)
print ts

There is more information in the .rst files under doc/. The documentation is built with Sphinx. If you have Sphinx installed, you can build the documentation using the Makefile in doc:

cd doc
make html

Then the built documentation will start at: doc/_build/html/index.html.