`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]. `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 `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 `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 , which has been fitted to `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 , `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.