tsfresh
functime has rewritten most of the time-series features extractors from tsfresh into Polars.
Approximately 80% of the implementations are optimized lazy queries.
The rest are eager implementations. The overall performance improvements compared to tsfresh ranges between 5x to 50x.
Speed ups depend on the size of the input, the feature, and whether common subplan elimination is invoked
(i.e. multiple lazy features are collected together). Moreover, windowed / grouped features in functime can be a further 100x faster than tsfresh.
Usage Example
import numpy as np
import polars as pl
from functime.feature_extraction.tsfresh import (
approximate_entropy
benford_correlation,
binned_entropy,
c3
)
sin_x = np.sin(np.arange(120))
# Pass series directly
entropy = approximate_entropy(
x=pl.Series("ts", sin_x),
run_length=5,
filtering_level=0.0
)
# Lazy operations
features = (
pl.LazyFrame({"ts": sin_x})
.select(
approximate_entropy=approximate_entropy(
pl.col("ts"),
run_length=5,
filtering_level=0.0
),
benford_correlation=benford_correlation(pl.col("ts")),
binned_entropy=binned_entropy(pl.col("ts"), bin_count=10),
c3=c3(),
)
.collect()
)
absolute_energy(x)
Compute the absolute energy of a time series.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x |
Expr | Series
|
Input time-series. |
required |
Returns:
| Type | Description |
|---|---|
float | Expr
|
|
absolute_maximum(x)
Compute the absolute maximum of a time series.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x |
Expr | Series
|
Input time-series. |
required |
Returns:
| Type | Description |
|---|---|
float | Expr
|
|
absolute_sum_of_changes(x)
Compute the absolute sum of changes of a time series.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x |
Expr | Series
|
Input time-series. |
required |
Returns:
| Type | Description |
|---|---|
float | Expr
|
|
approximate_entropy(x, run_length, filtering_level, scale_by_std=True)
Approximate sample entropies of a time series given the filtering level. This only works for Series input right now.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x |
Expr | Series
|
Input time-series. |
required |
run_length |
int
|
Length of compared run of data. This is |
required |
filtering_level |
float
|
Filtering level, must be positive. This is |
required |
scale_by_std |
bool
|
Whether to scale filter level by std of data. In most applications, this is the default behavior, but not in some other cases. |
True
|
Returns:
| Type | Description |
|---|---|
float
|
|
augmented_dickey_fuller(x, n_lags)
Calculates the Augmented Dickey-Fuller (ADF) test statistic. This only works for Series input right now.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x |
Expr | Series
|
Input time-series. |
required |
n_lags |
int
|
The number of lags to include in the test. |
required |
Returns:
| Type | Description |
|---|---|
float
|
|
autocorrelation(x, n_lags)
Calculate the autocorrelation for a specified lag.
The autocorrelation measures the linear dependence between a time-series and a lagged version of itself.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x |
Expr | Series
|
Input time-series. |
required |
n_lags |
int
|
The lag at which to calculate the autocorrelation. Must be a non-negative integer. |
required |
Returns:
| Type | Description |
|---|---|
float | Expr
|
Autocorrelation at the given lag. Returns None, if lag is less than 0. |
autoregressive_coefficients(x, n_lags)
Computes coefficients for an AR(n_lags) process. This only works for Series input
right now.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x |
Expr | Series
|
Input time-series. |
required |
n_lags |
int
|
The number of lags in the autoregressive process. |
required |
Returns:
| Type | Description |
|---|---|
list of float
|
|
benford_correlation(x)
Returns the correlation between the first digit distribution of the input time series and the Newcomb-Benford's Law distribution.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x |
Expr | Series
|
Input time-series. |
required |
Returns:
| Type | Description |
|---|---|
float | Expr
|
|
benford_correlation2(x)
Returns the correlation between the first digit distribution of the input time series and the Newcomb-Benford's Law distribution. This version may hit some float point precision issues for some rare numbers.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x |
Expr | Series
|
Input time-series. |
required |
Returns:
| Type | Description |
|---|---|
An expression for benford_correlation representing a float
|
|
binned_entropy(x, bin_count=10)
Calculates the entropy of a binned histogram for a given time series.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x |
Expr | Series
|
Input time-series. |
required |
bin_count |
int
|
The number of bins to use in the histogram. Default is 10. |
10
|
Returns:
| Type | Description |
|---|---|
float | Expr
|
|
c3(x, n_lags)
Measure of non-linearity in the time series using c3 statistics.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x |
Expr | Series
|
Input time-series. |
required |
n_lags |
int
|
The lag that should be used in the calculation of the feature. |
required |
Returns:
| Type | Description |
|---|---|
float | Expr
|
|
change_quantiles(x, q_low, q_high, is_abs)
First fixes a corridor given by the quantiles ql and qh of the distribution of x. It will return a list of changes coming from consecutive values that both lie within the quantile range. The user may optionally get abssolute value of the changes, and compute stats from these changes. If q_low >= q_high, it will return null.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x |
Expr | Series
|
A single time-series. |
required |
q_low |
float
|
The lower quantile of the corridor. Must be less than |
required |
q_high |
float
|
The upper quantile of the corridor. Must be greater than |
required |
is_abs |
bool
|
If True, takes absolute difference. |
required |
Returns:
| Type | Description |
|---|---|
list of float | Expr
|
|
cid_ce(x, normalize=False)
Computes estimate of time-series complexity[^1].
A more complex time series has more peaks and valleys. This feature is calculated by:
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x |
Expr | Series
|
A single time-series. |
required |
normalize |
bool
|
If True, z-normalizes the time-series before computing the feature. Default is False. |
False
|
Returns:
| Type | Description |
|---|---|
float | Expr
|
|
count_above(x, threshold=0.0)
Calculate the percentage of values above or equal to a threshold.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x |
Expr | Series
|
Input time-series. |
required |
threshold |
float
|
The threshold value for comparison. |
0.0
|
Returns:
| Type | Description |
|---|---|
float | Expr
|
|
count_above_mean(x)
Count the number of values that are above the mean.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x |
Expr | Series
|
Input time-series. |
required |
Returns:
| Type | Description |
|---|---|
int | Expr
|
|
count_below(x, threshold=0.0)
Calculate the percentage of values below or equal to a threshold.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x |
Expr | Series
|
Input time-series. |
required |
threshold |
float
|
The threshold value for comparison. |
0.0
|
Returns:
| Type | Description |
|---|---|
float | Expr
|
|
count_below_mean(x)
Count the number of values that are below the mean.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x |
Expr | Series
|
Input time-series. |
required |
Returns:
| Type | Description |
|---|---|
int | Expr
|
|
cwt_coefficients(x, widths=(2, 5, 10, 20), n_coefficients=14)
Calculates a Continuous wavelet transform for the Ricker wavelet.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x |
Expr | Series
|
Input time-series. |
required |
widths |
Sequence[int]
|
The widths of the Ricker wavelet to use for the CWT. Default is (2, 5, 10, 20). |
(2, 5, 10, 20)
|
n_coefficients |
int
|
The number of CWT coefficients to return. Default is 14. |
14
|
Returns:
| Type | Description |
|---|---|
list of float
|
|
energy_ratios(x, n_chunks=10)
Calculates sum of squares over the whole series for n_chunks equally segmented parts of the time-series.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x |
list of float
|
The time-series to be segmented and analyzed. |
required |
n_chunks |
int
|
The number of equally segmented parts to divide the time-series into. Default is 10. |
10
|
Returns:
| Type | Description |
|---|---|
list of float | Expr
|
|
fft_coefficients(x)
Calculates Fourier coefficients and phase angles of the the 1-D discrete Fourier Transform. This only works for Series input right now.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x |
Expr | Series
|
Input time series. |
required |
n_threads |
int
|
Number of threads to use. If None, uses all threads available. Defaults to None. |
required |
Returns:
| Type | Description |
|---|---|
dict of list of floats | Expr
|
|
first_location_of_maximum(x)
Returns the first location of the maximum value of x. The position is calculated relatively to the length of x.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x |
Expr | Series
|
Input time-series. |
required |
Returns:
| Type | Description |
|---|---|
float | Expr
|
|
first_location_of_minimum(x)
Returns the first location of the minimum value of x. The position is calculated relatively to the length of x.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x |
Expr | Series
|
Input time-series. |
required |
Returns:
| Type | Description |
|---|---|
float | Expr
|
|
fourier_entropy(x, n_bins=10)
Calculate the Fourier entropy of a time series. This only works for Series input right now.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x |
Expr | Series
|
Input time-series. |
required |
n_bins |
int
|
The number of bins to use for the entropy calculation. Default is 10. |
10
|
Returns:
| Type | Description |
|---|---|
float
|
|
friedrich_coefficients(x, polynomial_order=3, n_quantiles=30)
Calculate the Friedrich coefficients of a time series.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x |
TIME_SERIES_T
|
The time series to calculate the Friedrich coefficients of. |
required |
polynomial_order |
int
|
The order of the polynomial to fit to the quantile means. Default is 3. |
3
|
n_quantiles |
int
|
The number of quantiles to use for the calculation. Default is 30. |
30
|
Returns:
| Type | Description |
|---|---|
list of float
|
|
harmonic_mean(x)
Returns the harmonic mean of the expression
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x |
Expr | Series
|
Input time series. |
required |
Returns:
| Type | Description |
|---|---|
float | Expr
|
|
has_duplicate(x)
Check if the time-series contains any duplicate values.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x |
Expr | Series
|
Input time-series. |
required |
Returns:
| Type | Description |
|---|---|
bool | Expr
|
|
has_duplicate_max(x)
Check if the time-series contains any duplicate values equal to its maximum value.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x |
Expr | Series
|
Input time-series. |
required |
Returns:
| Type | Description |
|---|---|
bool | Expr
|
|
has_duplicate_min(x)
Check if the time-series contains duplicate values equal to its minimum value.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x |
Expr | Series
|
Input time-series. |
required |
Returns:
| Type | Description |
|---|---|
bool | Expr
|
|
index_mass_quantile(x, q)
Calculates the relative index i of time series x where q% of the mass of x lies left of i. For example for q = 50% this feature calculator will return the mass center of the time series.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x |
Expr | Series
|
Input time-series. |
required |
q |
float
|
The quantile. |
required |
Returns:
| Type | Description |
|---|---|
float | Expr
|
|
large_standard_deviation(x, ratio=0.25)
Checks if the time-series has a large standard deviation: std(x) > r * (max(X)-min(X)).
As a heuristic, the standard deviation should be a forth of the range of the values.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x |
Expr | Series
|
Input time-series. |
required |
ratio |
float
|
The ratio of the interval to compare with. |
0.25
|
Returns:
| Type | Description |
|---|---|
bool | Expr
|
|
last_location_of_maximum(x)
Returns the last location of the maximum value of x. The position is calculated relatively to the length of x.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x |
Expr | Series
|
Input time-series. |
required |
Returns:
| Type | Description |
|---|---|
float | Expr
|
|
last_location_of_minimum(x)
Returns the last location of the minimum value of x. The position is calculated relatively to the length of x.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x |
Expr | Series
|
Input time-series. |
required |
Returns:
| Type | Description |
|---|---|
float | Expr
|
|
lempel_ziv_complexity(x, n_bins)
Calculate a complexity estimate based on the Lempel-Ziv compression algorithm.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x |
Expr | Series
|
Input time-series. |
required |
n_bins |
int
|
An integer specifying the number of bins to use for discretizing the time series. |
required |
Returns:
| Type | Description |
|---|---|
list of float
|
|
linear_trend(x)
Compute the slope, intercept, and RSS of the linear trend.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x |
Expr | Series
|
Input time-series. |
required |
Returns:
| Type | Description |
|---|---|
Mapping[str, float] | Expr
|
|
longest_strike_above_mean(x)
Returns the length of the longest consecutive subsequence in x that is greater than the mean of x. If all values in x are null, 0 will be returned.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x |
Expr | Series
|
Input time-series. |
required |
Returns:
| Type | Description |
|---|---|
int | Expr
|
|
longest_strike_below_mean(x)
Returns the length of the longest consecutive subsequence in x that is smaller than the mean of x. If all values in x are null, 0 will be returned.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x |
Expr | Series
|
Input time-series. |
required |
Returns:
| Type | Description |
|---|---|
int | Expr
|
|
mean_abs_change(x)
Compute mean absolute change.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x |
Expr | Series
|
A single time-series. |
required |
Returns:
| Type | Description |
|---|---|
float | Expr
|
|
mean_change(x)
Compute mean change.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x |
Expr | Series
|
A single time-series. |
required |
Returns:
| Type | Description |
|---|---|
float | Expr
|
|
mean_n_absolute_max(x, n_maxima)
Calculates the arithmetic mean of the n absolute maximum values of the time series.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x |
Expr | Series
|
Input time-series. |
required |
n_maxima |
int
|
The number of maxima to consider. |
required |
Returns:
| Type | Description |
|---|---|
float | Expr
|
|
mean_second_derivative_central(x)
Returns the mean value of a central approximation of the second derivative.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x |
Series
|
A time series to calculate the feature of. |
required |
Returns:
| Type | Description |
|---|---|
Series
|
|
number_crossings(x, crossing_value=0.0)
Calculates the number of crossings of x on m, where m is the crossing value.
A crossing is defined as two sequential values where the first value is lower than m and the next is greater, or vice-versa. If you set m to zero, you will get the number of zero crossings.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x |
Expr | Series
|
A single time-series. |
required |
crossing_value |
float
|
The crossing value. Defaults to 0.0. |
0.0
|
Returns:
| Type | Description |
|---|---|
float | Expr
|
|
number_cwt_peaks(x, max_width=5)
Number of different peaks in x.
To estimate the numbers of peaks, x is smoothed by a ricker wavelet for widths ranging from 1 to n. This feature calculator returns the number of peaks that occur at enough width scales and with sufficiently high Signal-to-Noise-Ratio (SNR)
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x |
Series
|
A single time-series. |
required |
max_width : int maximum width to consider
Returns:
| Type | Description |
|---|---|
float
|
|
number_peaks(x, support)
Calculates the number of peaks of at least support n in the time series x. A peak of support n is defined as a subsequence of x where a value occurs, which is bigger than its n neighbours to the left and to the right.
Hence in the sequence
x = [3, 0, 0, 4, 0, 0, 13]
4 is a peak of support 1 and 2 because in the subsequences
[0, 4, 0] [0, 0, 4, 0, 0]
4 is still the highest value. Here, 4 is not a peak of support 3 because 13 is the 3th neighbour to the right of 4 and its bigger than 4.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x |
Expr | Series
|
Input time-series. |
required |
support |
int
|
Support of the peak |
required |
Returns:
| Type | Description |
|---|---|
int | Expr
|
|
percent_reoccuring_values(x)
Returns the percentage of values that are present in the time series more than once.
The percentage is calculated as follows:
len(different values occurring more than once) / len(different values)
This means the percentage is normalized to the number of unique values in the time series, in contrast to the
percent_reocurring_points function.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x |
Expr | Series
|
Input time-series. |
required |
Returns:
| Type | Description |
|---|---|
float | Expr
|
|
percent_reocurring_points(x)
Returns the percentage of non-unique data points in the time series. Non-unique data points are those that occur more than once in the time series.
The percentage is calculated as follows:
# of data points occurring more than once / # of all data points
This means the ratio is normalized to the number of data points in the time series, in contrast to the
percent_reoccuring_values function.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x |
Expr | Series
|
Input time-series. |
required |
Returns:
| Type | Description |
|---|---|
float
|
|
permutation_entropy(x, tau=1, n_dims=3, base=math.e)
Computes permutation entropy.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x |
Expr | Series
|
Input time-series. |
required |
tau |
int
|
The embedding time delay which controls the number of time periods between elements of each of the new column vectors. |
1
|
n_dims |
int, > 1
|
The embedding dimension which controls the length of each of the new column vectors |
3
|
base |
float
|
The base for log in the entropy computation |
e
|
Returns:
| Type | Description |
|---|---|
float | Expr
|
|
range_count(x, lower, upper, closed='left')
Computes values of input expression that is between lower (inclusive) and upper (exclusive).
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x |
Expr | Series
|
Input time-series. |
required |
lower |
float
|
The lower bound, inclusive |
required |
upper |
float
|
The upper bound, exclusive |
required |
closed |
ClosedInterval
|
Whether or not the boundaries should be included/excluded |
'left'
|
Returns:
| Type | Description |
|---|---|
int | Expr
|
|
ratio_beyond_r_sigma(x, ratio=0.25)
Returns the ratio of values in the series that is beyond r*std from mean on both sides.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x |
Expr | Series
|
Input time-series. |
required |
ratio |
float
|
The scaling factor for std |
0.25
|
Returns:
| Type | Description |
|---|---|
float | Expr
|
|
ratio_n_unique_to_length(x)
Calculate the ratio of the number of unique values to the length of the time-series.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x |
Expr | Series
|
Input time-series. |
required |
Returns:
| Type | Description |
|---|---|
float | Expr
|
|
root_mean_square(x)
Calculate the root mean square.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x |
Expr | Series
|
Input time-series. |
required |
Returns:
| Type | Description |
|---|---|
float | Expr
|
|
sample_entropy(x, ratio=0.2)
Calculate the sample entropy of a time series. This only works for Series input right now.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x |
Expr | Series
|
The input time series. |
required |
ratio |
float
|
The tolerance parameter. Default is 0.2. |
0.2
|
Returns:
| Type | Description |
|---|---|
float | Expr
|
|
spkt_welch_density(x, n_coeffs=None)
This estimates the cross power spectral density of the time series x at different frequencies. This only works for Series input right now.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x |
Expr | Series
|
The input time series. |
required |
n_coeffs |
Optional[int]
|
The number of coefficients you want to take. If none, will take all, which will be a list as long as the input time series. |
None
|
Returns:
| Type | Description |
|---|---|
list of floats
|
|
sum_reocurring_points(x)
Returns the sum of all data points that are present in the time series more than once.
For example, sum_reocurring_points(pl.Series([2, 2, 2, 2, 1])) returns 8, as 2 is a reoccurring value, so all 2's
are summed up.
This is in contrast to the sum_reocurring_values function, where each reoccuring value is only counted once.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x |
Expr | Series
|
Input time-series. |
required |
Returns:
| Type | Description |
|---|---|
float | Expr
|
|
sum_reocurring_values(x)
Returns the sum of all values that are present in the time series more than once.
For example, sum_reocurring_values(pl.Series([2, 2, 2, 2, 1])) returns 2, as 2 is a reoccurring value, so it is
summed up with all other reoccuring values (there is none), so the result is 2.
This is in contrast to the sum_reocurring_points function, where each reoccuring value is only counted as often as it is present in the data.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x |
Expr | Series
|
Input time-series. |
required |
Returns:
| Type | Description |
|---|---|
float | Expr
|
|
symmetry_looking(x, ratio=0.25)
Check if the distribution of x looks symmetric.
A distribution is considered symmetric if: | mean(X)-median(X) | < ratio * (max(X)-min(X))
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x |
Series
|
Input time-series. |
required |
ratio |
float
|
Multiplier on distance between max and min. |
0.25
|
Returns:
| Type | Description |
|---|---|
bool | Expr
|
|
time_reversal_asymmetry_statistic(x, n_lags)
Returns the time reversal asymmetry statistic.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x |
Series
|
Input time-series. |
required |
n_lags |
int
|
The lag that should be used in the calculation of the feature. |
required |
Returns:
| Type | Description |
|---|---|
float | Expr
|
|
var_gt_std(x, ddof=1)
Is the variance >= std? In other words, is var >= 1?
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x |
Expr | Series
|
Input time series. |
required |
ddof |
int
|
Delta Degrees of Freedom used when computing var/std. |
1
|
Returns:
| Type | Description |
|---|---|
bool | Expr
|
|
variation_coefficient(x)
Calculate the coefficient of variation (CV).
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x |
Expr | Series
|
Input time series. |
required |
Returns:
| Type | Description |
|---|---|
float | Expr
|
|