List of available features

The following table provides an overview of the available featurest in the current version of TSFEL.

tsfel.feature_extraction.features

abs_energy(signal)	Computes the absolute energy of the signal.
auc(signal, fs)	Computes the area under the curve of the signal computed with trapezoid rule.
autocorr(signal)	Calculates the first 1/e crossing of the autocorrelation function (ACF).
average_power(signal, fs)	Computes the average power of the signal.
calc_centroid(signal, fs)	Computes the centroid along the time axis.
calc_max(signal)	Computes the maximum value of the signal.
calc_mean(signal)	Computes mean value of the signal.
calc_median(signal)	Computes median of the signal.
calc_min(signal)	Computes the minimum value of the signal.
calc_std(signal)	Computes standard deviation (std) of the signal.
calc_var(signal)	Computes variance of the signal.
dfa(signal)	Computes the Detrended Fluctuation Analysis (DFA) of the signal.
distance(signal)	Computes signal traveled distance.
ecdf(signal[, d])	Computes the values of ECDF (empirical cumulative distribution function) along the time axis.
ecdf_percentile(signal[, percentile])	Computes the percentile value of the ECDF.
ecdf_percentile_count(signal[, percentile])	Computes the cumulative sum of samples that are less than the percentile.
ecdf_slope(signal[, p_init, p_end])	Computes the slope of the ECDF between two percentiles.
entropy(signal[, prob])	Computes the entropy of the signal using the Shannon Entropy.
fft_mean_coeff(signal, fs[, nfreq])	Computes the mean value of each spectrogram frequency.
fundamental_frequency(signal, fs)	Computes fundamental frequency of the signal.
higuchi_fractal_dimension(signal)	Computes the fractal dimension of a signal using Higuchi's method (HFD).
hist(signal[, nbins, r])	Computes histogram of the signal.
human_range_energy(signal, fs)	Computes the human range energy ratio.
hurst_exponent(signal)	Computes the Hurst exponent of the signal through the Rescaled range (R/S) analysis.
interq_range(signal)	Computes interquartile range of the signal.
kurtosis(signal)	Computes kurtosis of the signal.
lempel_ziv(signal[, threshold])	Computes the Lempel-Ziv's (LZ) complexity index, normalized by the signal's length.
lpcc(signal[, n_coeff])	Computes the linear prediction cepstral coefficients.
max_frequency(signal, fs)	Computes maximum frequency of the signal.
max_power_spectrum(signal, fs)	Computes maximum power spectrum density of the signal.
maximum_fractal_length(signal)	Computes the Maximum Fractal Length (MFL) of the signal, which is the average length at the smallest scale, measured from the logarithmic plot determining FD.
mean_abs_deviation(signal)	Computes mean absolute deviation of the signal.
mean_abs_diff(signal)	Computes mean absolute differences of the signal.
mean_diff(signal)	Computes mean of differences of the signal.
median_abs_deviation(signal)	Computes median absolute deviation of the signal.
median_abs_diff(signal)	Computes median absolute differences of the signal.
median_diff(signal)	Computes median of differences of the signal.
median_frequency(signal, fs)	Computes median frequency of the signal.
mfcc(signal, fs[, pre_emphasis, nfft, ...])	Computes the MEL cepstral coefficients.
mse(signal[, m, maxscale, tolerance])	Computes the Multiscale entropy (MSE) of the signal, that performs the entropy analysis over multiple time scales.
negative_turning(signal)	Computes number of negative turning points of the signal.
neighbourhood_peaks(signal[, n])	Computes the number of peaks from a defined neighbourhood of the signal.
petrosian_fractal_dimension(signal)	Computes the Petrosian Fractal Dimension of a signal.
pk_pk_distance(signal)	Computes the peak to peak distance.
positive_turning(signal)	Computes number of positive turning points of the signal.
power_bandwidth(signal, fs)	Computes power spectrum density bandwidth of the signal.
rms(signal)	Computes root mean square of the signal.
skewness(signal)	Computes skewness of the signal.
slope(signal)	Computes the slope of the signal.
spectral_centroid(signal, fs)	Barycenter of the spectrum.
spectral_decrease(signal, fs)	Represents the amount of decreasing of the spectra amplitude.
spectral_distance(signal, fs)	Computes the signal spectral distance.
spectral_entropy(signal, fs)	Computes the spectral entropy of the signal based on Fourier transform.
spectral_kurtosis(signal, fs)	Measures the flatness of a distribution around its mean value.
spectral_positive_turning(signal, fs)	Computes number of positive turning points of the fft magnitude signal.
spectral_roll_off(signal, fs)	Computes the spectral roll-off of the signal.
spectral_roll_on(signal, fs)	Computes the spectral roll-on of the signal.
spectral_skewness(signal, fs)	Measures the asymmetry of a distribution around its mean value.
spectral_slope(signal, fs)	Computes the spectral slope.
spectral_spread(signal, fs)	Measures the spread of the spectrum around its mean value.
spectral_variation(signal, fs)	Computes the amount of variation of the spectrum along time.
sum_abs_diff(signal)	Computes sum of absolute differences of the signal.
wavelet_abs_mean(signal[, function, widths])	Computes CWT absolute mean value of each wavelet scale.
wavelet_energy(signal[, function, widths])	Computes CWT energy of each wavelet scale.
wavelet_entropy(signal[, function, widths])	Computes CWT entropy of the signal.
wavelet_std(signal[, function, widths])	Computes CWT std value of each wavelet scale.
wavelet_var(signal[, function, widths])	Computes CWT variance value of each wavelet scale.
zero_cross(signal)	Computes Zero-crossing rate of the signal.