Overview of an hctsa analysis

At its core, hctsa analysis involves computing a library of time-series analysis features (which we call operations) on a time-series dataset.

The basic sequence of a Matlab-based hctsa analysis is to: 1. Initialize a HCTSA.mat file, which contains all of the information about the set of time series and operations in your analysis, as well as the results of applying all operations to all time series, using TS_Init,

1. These operations can be computed on your time-series data using TS_Compute. The results are structured in the local HCTSA.mat file containing matrices (that store the results of the computations) and the tables (that store information about the time-series data and operations), as described here.

2. After the computation is complete, a range of processing, analysis, and plotting functions are provided to understand and interpret the results.

Example 1: Compute a feature vector for a time series

As a quick check of your operation library, you can compute the full default code library on a time-series data vector (a column vector of real numbers) as follows:

x = randn(500,1); % A random time-series
featVector = TS_CalculateFeatureVector(x,false); % compute the default feature vector for x

Example 2: Analyze a time-series dataset

Suppose you have have a time-series dataset to analyze. You first generate a formatted INP_ts.mat input file containing your time series data and associated name and keyword labels, as described here. You then initialize an hctsa calculation using the default library of features:

TS_Init('INP_ts.mat');

This generates a local file, HCTSA.mat containing the associated metadata for your time series, as well as information about the full time-series feature library (Operations) and the set of functions and code to call to evaluate them (MasterOperations), as described here.

Next you want to evaluate the code on all of the time series in your dataset. For this you can simply run:

TS_Compute;

As described here, or, for larger datasets, using a script to regularly save back to the local file (cf. sample_runscript_matlab).

Having run your calculations, you may then want to label your data using the keywords you provided in the case that you have labeled groups of time series:

TS_LabelGroups;

and then normalize and filter the data using the default sigmoidal transformation:

TS_Normalize;

A range of visualization scripts are then available to analyze the results, such as plotting the reordered data matrix:

TS_Cluster; % compute a reordering of data and features
TS_PlotDataMatrix; % plot the data matrix

To inspect a low-dimensional representation of the data:

TS_PlotLowDim;

Or to determine which features are best at classifying the labeled groups of time series in your dataset:

TS_TopFeatures;

Each of these functions can be run with a range of input settings.