Here we provide a full list of Matlab code files, organized loosely into broad categories, and with brief descriptions.
The full default set of over 7700 features in hctsa is produced by running all of the code files below, many of which produce a large number of outputs (e.g., some functions fit a timeseries model and then output statistics including the parameters of the bestfitting model, measures of the model's goodness of fit, the optimal model order, and autocorrelation statistics on the residuals). In our default feature set, each function is also run with different sets of input parameters, with each parameter set yielding characteristic outputs. For example, different inputs to CO_AutoCorr
determine the method in which autocorrelation is computed, as well as the time lag at which autocorrelation is calculated, e.g., lag 1, lag 2, lag 3, etc.; WL_dwtcoeff
has inputs that set the mother wavelet to use, and level of wavelet decomposition; FC_LocalSimple
has inputs that determine the timeseries forecasting method to use and the size of the training window. The set of code files below and their input parameters that define the default hctsa feature set are in the INP_mops.txt
file of the hctsa repository.
Code summarizing properties of the distribution of values in a time series (disregarding their sequence through time).
Code file  Description 
 Burstiness statistic of a time series. 
 Fits a distribution to data. 
 Custom skewness measures. 
 Statistics of a kernelsmoothed distribution of the data. 
 Maximum likelihood distribution fit to data. 
 The highlowmu statistic. 
 Mode of a data vector. 
 A given measure of location of a data vector. 
 The maximum and minimum values of the input data vector 
 A moment of the distribution of the input time series. 
 How statistics depend on distributional outliers. 
 How distributional statistics depend on distributional outliers. 
 Proportion of values in a timeseries vector. 
 Quantiles of the distribution of values in the time series data vector. 
 How timeseries properties change as points are removed. 
 Fit distributions or simple timeseries models to the data. 
 Measure of spread of the input time series. 
 Mean of the trimmed time series using 
 The proportion of the time series that are unique values. 
 Proportion of data points within p standard deviations of the mean. 
 Coefficient of variation. 
 Distance from the mean at which a given proportion of data are more distant. 
 Distributional entropy. 
 Hypothesis test for distributional fits to a data vector. 
Code summarizing basic properties of how values of a time series are correlated through time.
Code file  Description 
 Changes in the automutual information with the addition of noise. 
 Compute the autocorrelation of an input time series. 
 How the autocorrelation function changes with the time lag. 
 Statistics of the time series in a 2dimensional embedding space. 
 Angle autocorrelation in a 2dimensional embedding space. 
 Point density statistics in a 2d embedding space. 
 Analyzes distances in a 2d embedding space of a time series. 
 Shapebased statistics in a 2d embedding space. 
 Time of first minimum in a given correlation function. 
 The first zerocrossing of a given autocorrelation function. 
 A custom nonlinear autocorrelation of a time series. 
 Analysis of lineofsight angles between timeseries data points. 
 Statistics on datapoints inside geometric shapes across the time series. 
 The 1/e correlation length. 
 The first zerocrossing of the generalized selfcorrelation function. 
 The generalized linear selfcorrelation function of a time series. 
 Normalized nonlinear autocorrelation function, 
 Normalized nonlinear autocorrelation, 
 Computes James Theiler's crinkle statistic. 
 Computes Theiler's Q statistic. 
 Time reversal asymmetry statistic. 
 Principal Components analysis of a time series in an embedding space. 
Automutual information:  â€‹ 
 Automutual information (Rudy Moddemeijer implementation). 
 Variability in first minimum of automutual information. 
 The automutual information of the distribution using histograms. 
 Statistics on automutual information function for a time series. 
Entropy and complexity measures for time series
Code file  Description 
 Approximate Entropy of a time series. 
 Simple complexity measure of a time series. 
 LempelZiv complexity of a nbit encoding of a time series. 
 Approximate Shannon entropy of a time series. 
 Permutation Entropy of a time series. 
 Entropy of a time series using Rudy Moddemeijer's code. 
 How timeseries properties change with increasing randomization. 
 Sample Entropy of a time series. 
 Multiscale entropy of a time series. 
 Recurrence period density entropy (RPDE). 
 Entropy of time series using wavelets. 
Fitting timeseries models, and doing simple forecasting on time series.
Code file  Description 
 Compares a range of ARMA models fitted to a time series. 
 Fits an AR model of a given order, p. 
 Compares model fits of various orders to a time series. 
 Robustness of testset goodness of fit. 
 Exponential smoothing timeseries prediction model. 
 Robustness of model parameters across different segments of a time series. 
 Comparison of GARCH timeseries models. 
 GARCH timeseries modeling. 
 Gaussian Process timeseries modeling for local prediction. 
 Gaussian Process timeseries model for local prediction. 
 Gaussian Process timeseries model parameters and goodness of fit. 
 Change in goodness of fit across different state space models. 
 State space timeseries model fitting. 
 Statistics of a fitted AR model to a time series. 
 Statistics on a fitted ARMA model. 
 Hidden Markov Model (HMM) fitting to a time series. 
 Fits a Hidden Markov Model to sequential data. 
 Goodness of model predictions across prediction lengths. 
 Simple local timeseries forecasting. 
 How simple local forecasting depends on window length. 
 How surprised you would be of the next data point given recent memory. 
 Investigates whether AR model fit improves with different preprocessings. 
Quantifying how properties of a time series change over time.
Code file  Description 
 Mean and variance in local timeseries subsegments. 
 How stationarity estimates depend on the number of timeseries subsegments. 
 The KPSS stationarity test. 
 Compares the distribution in consecutive timeseries segments. 
 Compares local statistics to global statistics of a time series. 
 PhillipsPeron unit root test. 
 How the timeseries range changes across time. 
 Sliding window measures of stationarity. 
 Bootstrapbased stationarity measure. 
 Simple meanstationarity metric, 
 Standard deviation of the nth derivative of the time series. 
 How the output of 
 Crossforecast errors of zerothorder timeseries models. 
 Variance ratio test for random walk. 
Step detection:  â€‹ 
 Analysis of discrete steps in a time series. 
 Dependence of step detection on regularization parameter. 
 Variance change points in a time series. 
Nonlinear timeseries analysis methods, including embedding dimensions and fluctuation analysis.
Code file  Description 
 Correlation dimension of a time series. 
 Delay Vector Variance method for real and complex signals. 
 False nearest neighbors of a time series. 
 Normalized droponeout constant interpolation nonlinear prediction error. 
 Information dimension. 


 False nearest neighbors of a time series. 
 Fractal dimension spectrum, 
 Correlation sum scaling by GrassbergerProccacia algorithm. 
 Largest Lyapunov exponent of a time series. 
 Poincare sectino analysis of a time series. 
 Analysis of the histogram of return times. 
 Taken's estimator for correlation dimension. 


 Box counting, information, and correlation dimension of a time series. 
 Analyzes the falsenearest neighbors statistic. 
 Analyzes test statistics obtained from surrogate time series. 
 Surrogate timeseries analysis. 
 Optimal delay time using the method of Parlitz and Wichard. 
 Local density estimates in the timedelay embedding space. 
Fluctuation analysis:  â€‹ 
 Physionet implementation of multiscale multifractal analysis 
 Matlab wrapper for Max Little's 
 Implements fluctuation analysis by a variety of methods. 
Properties of the timeseries power spectrum, wavelet spectrum, and other periodicity measures.
Code file  Description 
 Statistics of the power spectrum of a time series. 
 A simple test of seasonality. 
 Periodicity extraction measure of Wang et al. 
 Detail coefficients of a wavelet decomposition. 
 Wavelet decomposition of the time series. 
 Continuous wavelet transform of a time series. 
 Discrete wavelet transform coefficients. 
 Parameters of fractional Gaussian noise/Brownian motion in a time series. 
 Frequency components in a periodic time series. 
Properties of a discrete symbolization of a time series.
Code file  Description 
 Statistics on a binary symbolization of the time series. 
 Characterizes stretches of 0/1 in timeseries binarization. 
 Motifs in a coarsegraining of a time series to a 3letter alphabet. 
 Local motifs in a binary symbolization of the time series. 
 Transition probabilities between different timeseries states. 
 How transition probabilities change with alphabet size. 
Simple timeseries properties derived mostly from the heart rate variability (HRV) literature.
Code file  Description 
 Classic heart rate variability (HRV) statistics. 


 The 
 Heart rate variability (HRV) measures of a time series. 
Basic statistics of a time series, including measures of trend.
Code file  Description 
 Quantifies various measures of trend in a time series. 
 Goodness of a polynomial fit to a time series. 
 Length of an input data vector. 
 How local maximums and minimums vary across the time series. 
 Correlations between simple statistics in local windows of a time series. 
 Basic statistics about an input time series. 
Other properties, like extreme values, visibility graphs, physicsbased simulations, and dependence on preprocessings applied to a time series.
Code file  Description 
 Moving threshold model for extreme events in a time series. 
 Statistical hypothesis test applied to a time series. 
 Visibility graph analysis of a time series. 
 Couples the values of the time series to a dynamical system. 
 Simulates a hypothetical walker moving through the time domain. 
 Compare how timeseries properties change after preprocessing. 
 How timeseries properties change in response to iterative preprocessing. 