<commit_before>[![Build Status](https://travis-ci.org/sryza/spark-timeseries.svg)](https://travis-ci.org/sryza/spark-timeseries)

Time Series for Spark (The `spark-ts` Package)
=============

A Scala / Java / Python library for interacting with time series data on Apache Spark.

Docs are available at http://sryza.github.io/spark-timeseries.

Scaladoc is available at http://sryza.github.io/spark-timeseries/0.2.0/scaladocs/index.html.

Javadoc is available at http://sryza.github.io/spark-timeseries/0.2.0/apidocs/index.html.

Python doc is available at http://sryza.github.io/spark-timeseries/0.2.0/pydoc/py-modindex.html.

The aim here is to provide
* A set of abstractions for manipulating large time series data sets, similar to
  what's provided for smaller data sets in
  [Pandas](http://pandas.pydata.org/pandas-docs/dev/timeseries.html),
  [Matlab](http://www.mathworks.com/help/matlab/time-series.html), and R's
  [zoo](http://cran.r-project.org/web/packages/zoo/index.html) and 
  [xts](http://cran.r-project.org/web/packages/xts/index.html) packages.
* Models, tests, and functions that enable dealing with time series from a statistical perspective,
  similar to what's provided in [StatsModels](http://statsmodels.sourceforge.net/devel/tsa.html)
  and a variety of Matlab and R packages.

The library is geared towards use cases in finance (munging tick data, building risk models), but
intends to be general enough that other fields with continuous time series data, like meteorology,<commit_msg>Post reference to the Google group in README<commit_after>[![Build Status](https://travis-ci.org/sryza/spark-timeseries.svg)](https://travis-ci.org/sryza/spark-timeseries)

Time Series for Spark (The `spark-ts` Package)
=============

A Scala / Java / Python library for interacting with time series data on Apache Spark.

Post questions and comments to the [Google group](https://groups.google.com/forum/#!forum/spark-ts), or email it directly at <mailto:<EMAIL>>.

Docs are available at http://sryza.github.io/spark-timeseries.

Or check out the [Scaladoc](http://sryza.github.io/spark-timeseries/0.2.0/scaladocs/index.html), [Javadoc](http://sryza.github.io/spark-timeseries/0.2.0/apidocs/index.html), or [Python doc](http://sryza.github.io/spark-timeseries/0.2.0/pydoc/py-modindex.html).

The aim here is to provide
* A set of abstractions for manipulating large time series data sets, similar to
  what's provided for smaller data sets in
  [Pandas](http://pandas.pydata.org/pandas-docs/dev/timeseries.html),
  [Matlab](http://www.mathworks.com/help/matlab/time-series.html), and R's
  [zoo](http://cran.r-project.org/web/packages/zoo/index.html) and 
  [xts](http://cran.r-project.org/web/packages/xts/index.html) packages.
* Models, tests, and functions that enable dealing with time series from a statistical perspective,
  similar to what's provided in [StatsModels](http://statsmodels.sourceforge.net/devel/tsa.html)
  and a variety of Matlab and R packages.

The library is geared towards use cases in finance (munging tick data, building risk models), but
intends to be general enough that other fields with continuous time series data, like meteorology,
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