Highly comparable time-series analysis in Nitime

The aim of this project was to demonstrate that an existing Matlab-based package for implementing thousands of time-series analysis methods, hctsa could be extended to a python-based implementation, for potential future inclusion into Nitime.<br>Recent work has contributed a comprehensive library of over 35,000 pieces of diverse time-series data, and over 7,000 unique structural features extracted from hundreds of different time-series analysis methods which can be explored through an associated website and implemented using the Matlab-based code package, hctsa.<br>The hctsa software provides a systematic, algorithmic platform for computing a wide range of structural properties from a single time series, including basic statistics of the distribution, linear correlation structure, stationarity, information theoretic and entropy measures, methods from the physical nonlinear time-series analysis literature, linear and nonlinear model fits, and others.<br>Thus, hctsa can be used to map a time series to a comprehensive vector of interpretable structural features and these features can then be systematically compared to determine and understand the most useful features for a given scientific objective (e.g., features of an EEG signal that help classify different patient groups).<br>In order to apply highly comparative time-series analysis in the neuroscience community, it would be desirable to implement some time-series analysis methods into Nitime, a python-based software package for performing time-series analysis on neuroscience data.<br>Implementation of useful time-series features into python, and potential integration with Nitime, would not only facilitate their use by the neuroscience community, but also their maintenance and development within an open source framework.<br>Although there are no plans to reimplement the full hctsa feature library in python, our hope is that published work describing useful time-series features (discovered using the hctsa library) can also contribute a python implementation, to promote its use by the neuroscience community.