Assigning group labels to data
hctsa allows you to map time-series data into a unified feature space, a representation that is well suited to then applying machine learning methods to perform classification where the goal is to predict a class label assigned to each time series from features of its dynamics.
Class labels can be assigned to time series in an hctsa dataset using the function TS_LabelGroups, which populates the Group column of the TimeSeries table as a categorical labeling of the data (the class labels the classifier will attempt to predict). These group labels are used by a range of analysis functions, including TS_PlotLowDim, TS_TopFeatures, and TS_Classify.
The machinery of TS_LabelGroups assumes that the class labels you are interested in are contained in the Keywords column of the TimeSeries table (set up from your original input file when TS_Init was run).


Manual labeling

If you want to label according to a specific set of keywords, you can do this by specifying the keywords that define the unique groups. The example below assigns labels to two groups of time series in the HCTSA.mat (specifying the shorthand 'raw' for this default, un-normalized data), corresponding to those labeled as 'parkinsons' and those labeled as 'healthy':
Note that any time series that are not labeling by either 'parkinsons' nor 'healthy' are left unlabeled.

Automatic labeling

If every time series has a single keyword that uniquely labels it, TS_LabelGroups can automatically detect this by running TS_LabelGroups('raw',{});.

Complex labeling

More complex labeling (e.g., using custom combinations of keywords) is not implemented in TS_LabelGroups, but can be achieved by writing a script that does logical operations on calls to TS_LabelGroups and saves back to the TimeSeries.Group column. Here is an example of combining two labels (male/female and day/night) on fly movement data.

Working with labels

By default, the group labels are saved back to the data file (in this example, HCTSA.mat).
Group labels can be reassigned at any time by re-running the TS_LabelGroups function, and can be cleared by running, e.g., TS_LabelGroups('raw','clear').
Assigned labels are used by the analysis, plotting, and classification functions of hctsa.
Note: If you assign a labeling to a given dataset (e.g., HCTSA.mat), then this labeling will remain with the normalized data (e.g., after running TS_Normalize).