WARNING

The scikits.timeseries module is no longer undergoing active development. There is an outstanding list of bugs that are unlikely to be fixed. The plan is for the core functionality of this module to be implemented in pandas. If you wish to see this module live on independently of pandas, feel free to fork the code and take it over.

Welcome

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.

Documentation

Contents for a complete overview

Search page search the documentation

General Index all functions, classes, terms

Get scikits.timeseries

Download scikits.timeseries from the sourceforge project page (but first take a look at the installion page).

The code can be found in a subvesion repository, at http://svn.scipy.org/svn/scikits/trunk/timeseries.