The scikits.timeseries module provides classes and functions for manipulating, reporting, and plotting time series of various frequencies. The focus is on convenient data access and manipulation while leveraging the existing mathematical functionality in numpy and scipy.
If the following scenarios sound familiar to you, then you will likely find the scikits.timeseries module useful:
- Compare many time series with different ranges of data (eg. stock prices);
- Create time series plots with intelligently spaced axis labels;
- Convert a daily time series to monthly by taking the average value during each month;
- Work with data that has missing values;
- Determine the last business day of the previous month/quarter/year for reporting purposes;
- Compute a moving standard deviation efficiently.
These are just some of the scenarios that are made very simple with the scikits.timeseries module.
In order to use the scikits.timeseries package, the following external packages must be installed beforehand:
- Python 2.4 or later.
Please note that Python 3 is not supported yet.
- setuptools
scikits is a namespace package, and as a result every scikit requires setuptools to be installed to function properly.
- Numpy 1.3.0 or later.
Numpy is a library to manipulate large arrays of numerical data. Version 1.3.0 provides improved support to numpy.ma.MaskedArray objects with structured datatype.
The following packages are strongly recommended:
- SciPy 0.7.0 or later:
SciPy is a set of Numpy-based tools for engineering and scientific applications. Some of the scikits.timeseries.lib sub-modules (lib.interpolate, lib.moving_funcs...) use SciPy interpolation and signal functions.
- matplotlib 0.98.0 or later:
matplotlib is a Python 2D plotting library. scikits.timeseries includes some extensions to matplotlib to plot time series.
- PyTables 2.0 or later:
PyTables is a package for managing hierarchical datasets, using the HDF5 format. scikits.timeseries provides support to store time series with missing data.
You can download source code and windows installers from the sourceforge project page.
For svn repository access:
svn co http://svn.scipy.org/svn/scikits/trunk/timeseries timeseriesTo install on windows, it is recommend that you use the pre-built installers from the sourceforge project page.
To install the scikits.timeseries package directly from source, run the command:
python setup.py install