abs_energy(x)

Returns the absolute energy of the time series which is the sum over the squared values

absolute_maximum(x)

Calculates the highest absolute value of the time series x.

absolute_sum_of_changes(x)

Returns the sum over the absolute value of consecutive changes in the series x

agg_autocorrelation(x, param)

Descriptive statistics on the autocorrelation of the time series.

agg_linear_trend(x, param)

Calculates a linear least-squares regression for values of the time series that were aggregated over chunks versus the sequence from 0 up to the number of chunks minus one.

approximate_entropy(x, m, r)

Implements a vectorized Approximate entropy algorithm.

ar_coefficient(x, param)

This feature calculator fits the unconditional maximum likelihood of an autoregressive AR(k) process.

augmented_dickey_fuller(x, param)

Does the time series have a unit root?

autocorrelation(x, lag)

Calculates the autocorrelation of the specified lag, according to the formula [1]

benford_correlation(x)

Useful for anomaly detection applications [1][2]. Returns the correlation from first digit distribution when

binned_entropy(x, max_bins)

First bins the values of x into max_bins equidistant bins.

c3(x, lag)

Uses c3 statistics to measure non linearity in the time series

change_quantiles(x, ql, qh, isabs, f_agg)

First fixes a corridor given by the quantiles ql and qh of the distribution of x.

cid_ce(x, normalize)

This function calculator is an estimate for a time series complexity [1] (A more complex time series has more peaks, valleys etc.).

count_above(x, t)

Returns the percentage of values in x that are higher than t

count_above_mean(x)

Returns the number of values in x that are higher than the mean of x

count_below(x, t)

Returns the percentage of values in x that are lower than t

count_below_mean(x)

Returns the number of values in x that are lower than the mean of x

cwt_coefficients(x, param)

Calculates a Continuous wavelet transform for the Ricker wavelet, also known as the "Mexican hat wavelet" which is defined by

energy_ratio_by_chunks(x, param)

Calculates the sum of squares of chunk i out of N chunks expressed as a ratio with the sum of squares over the whole series.

fft_aggregated(x, param)

Returns the spectral centroid (mean), variance, skew, and kurtosis of the absolute fourier transform spectrum.

fft_coefficient(x, param)

Calculates the fourier coefficients of the one-dimensional discrete Fourier Transform for real input by fast fourier transformation algorithm

first_location_of_maximum(x)

Returns the first location of the maximum value of x.

first_location_of_minimum(x)

Returns the first location of the minimal value of x.

fourier_entropy(x, bins)

Calculate the binned entropy of the power spectral density of the time series (using the welch method).

friedrich_coefficients(x, param)

Coefficients of polynomial h(x), which has been fitted to the deterministic dynamics of Langevin model

has_duplicate(x)

Checks if any value in x occurs more than once

has_duplicate_max(x)

Checks if the maximum value of x is observed more than once

has_duplicate_min(x)

Checks if the minimal value of x is observed more than once

index_mass_quantile(x, param)

Calculates the relative index i of time series x where q% of the mass of x lies left of i.

kurtosis(x)

Returns the kurtosis of x (calculated with the adjusted Fisher-Pearson standardized moment coefficient G2).

large_standard_deviation(x, r)

Does time series have large standard deviation?

last_location_of_maximum(x)

Returns the relative last location of the maximum value of x.

last_location_of_minimum(x)

Returns the last location of the minimal value of x.

lempel_ziv_complexity(x, bins)

Calculate a complexity estimate based on the Lempel-Ziv compression algorithm.

length(x)

Returns the length of x

linear_trend(x, param)

Calculate a linear least-squares regression for the values of the time series versus the sequence from 0 to length of the time series minus one.

linear_trend_timewise(x, param)

Calculate a linear least-squares regression for the values of the time series versus the sequence from 0 to length of the time series minus one.

longest_strike_above_mean(x)

Returns the length of the longest consecutive subsequence in x that is bigger than the mean of x

longest_strike_below_mean(x)

Returns the length of the longest consecutive subsequence in x that is smaller than the mean of x

matrix_profile(x, param)

Calculates the 1-D Matrix Profile[1] and returns Tukey's Five Number Set plus the mean of that Matrix Profile.

max_langevin_fixed_point(x, r, m)

Largest fixed point of dynamics :math:argmax_x {h(x)=0}` estimated from polynomial h(x), which has been fitted to the deterministic dynamics of Langevin model

maximum(x)

Calculates the highest value of the time series x.

mean(x)

Returns the mean of x

mean_abs_change(x)

Average over first differences.

mean_change(x)

Average over time series differences.

mean_n_absolute_max(x, number_of_maxima)

Calculates the arithmetic mean of the n absolute maximum values of the time series.

mean_second_derivative_central(x)

Returns the mean value of a central approximation of the second derivative

median(x)

Returns the median of x

minimum(x)

Calculates the lowest value of the time series x.

number_crossing_m(x, m)

Calculates the number of crossings of x on m.

number_cwt_peaks(x, n)

Number of different peaks in x.

number_peaks(x, n)

Calculates the number of peaks of at least support n in the time series x.

partial_autocorrelation(x, param)

Calculates the value of the partial autocorrelation function at the given lag.

percentage_of_reoccurring_datapoints_to_all_datapoints(x)

Returns the percentage of non-unique data points.

percentage_of_reoccurring_values_to_all_values(x)

Returns the percentage of values that are present in the time series more than once.

permutation_entropy(x, tau, dimension)

Calculate the permutation entropy.

quantile(x, q)

Calculates the q quantile of x.

query_similarity_count(x, param)

This feature calculator accepts an input query subsequence parameter, compares the query (under z-normalized Euclidean distance) to all subsequences within the time series, and returns a count of the number of times the query was found in the time series (within some predefined maximum distance threshold).

range_count(x, min, max)

Count observed values within the interval [min, max).

ratio_beyond_r_sigma(x, r)

Ratio of values that are more than r * std(x) (so r times sigma) away from the mean of x.

ratio_value_number_to_time_series_length(x)

Returns a factor which is 1 if all values in the time series occur only once, and below one if this is not the case.

root_mean_square(x)

Returns the root mean square (rms) of the time series.

sample_entropy(x)

Calculate and return sample entropy of x.

set_property(key, value)

This method returns a decorator that sets the property key of the function to value

skewness(x)

Returns the sample skewness of x (calculated with the adjusted Fisher-Pearson standardized moment coefficient G1).

spkt_welch_density(x, param)

This feature calculator estimates the cross power spectral density of the time series x at different frequencies.

standard_deviation(x)

Returns the standard deviation of x

sum_of_reoccurring_data_points(x)

Returns the sum of all data points, that are present in the time series more than once.

sum_of_reoccurring_values(x)

Returns the sum of all values, that are present in the time series more than once.

sum_values(x)

Calculates the sum over the time series values

symmetry_looking(x, param)

Boolean variable denoting if the distribution of x looks symmetric.

time_reversal_asymmetry_statistic(x, lag)

Returns the time reversal asymmetry statistic.

value_count(x, value)

Count occurrences of value in time series x.

variance(x)

Returns the variance of x

variance_larger_than_standard_deviation(x)

Is variance higher than the standard deviation?

variation_coefficient(x)

Returns the variation coefficient (standard error / mean, give relative value of variation around mean) of x.