tsfeatures 0.2.0

Calculates various features from time series data.

tsfeatures

Calculates various features from time series data. Python implementation of the R package tsfeatures.

Installation

You can install the released version of tsfeatures from the Python package index with:

pip install tsfeatures

Usage

The tsfeatures main function calculates by default the features used by Montero-Manso, Talagala, Hyndman and Athanasopoulos in their implementation of the FFORMA model.

from tsfeatures import tsfeatures

This function receives a panel pandas df with columns unique_id, ds, y and optionally the frequency of the data.

tsfeatures(panel, freq=7)

By default (freq=None) the function will try to infer the frequency of each time series (using infer_freq from pandas on the ds column) and assign a seasonal period according to the built-in dictionary FREQS:

FREQS = {'H': 24, 'D': 1,
         'M': 12, 'Q': 4,
         'W':1, 'Y': 1}

You can use your own dictionary using the dict_freqs argument:

tsfeatures(panel, dict_freqs={'D': 7, 'W': 52})

List of available features

Features
acf_features	heterogeneity	series_length
arch_stat	holt_parameters	sparsity
count_entropy	hurst	stability
crossing_points	hw_parameters	stl_features
entropy	intervals	unitroot_kpss
flat_spots	lumpiness	unitroot_pp
frequency	nonlinearity
guerrero	pacf_features

See the docs for a description of the features. To use a particular feature included in the package you need to import it:

from tsfeatures import acf_features

tsfeatures(panel, freq=7, features=[acf_features])

You can also define your own function and use it together with the included features:

def number_zeros(x, freq):

    number = (x == 0).sum()
    return {'number_zeros': number}

tsfeatures(panel, freq=7, features=[acf_features, number_zeros])

tsfeatures can handle functions that receives a numpy array x and a frequency freq (this parameter is needed even if you don't use it) and returns a dictionary with the feature name as a key and its value.

R implementation

You can use this package to call tsfeatures from R inside python (you need to have installed R, the packages forecast and tsfeatures; also the python package rpy2):

from tsfeatures.tsfeatures_r import tsfeatures_r

tsfeatures(panel, freq=7, features=["acf_features"])

Observe that this function receives a list of strings instead of a list of functions.

Comparison with the R implementation (sum of absolute differences)

Non-seasonal data (100 Daily M4 time series)

feature	diff	feature	diff	feature	diff	feature	diff
e_acf10	0	e_acf1	0	diff2_acf1	0	alpha	3.2
seasonal_period	0	spike	0	diff1_acf10	0	arch_acf	3.3
nperiods	0	curvature	0	x_acf1	0	beta	4.04
linearity	0	crossing_points	0	nonlinearity	0	garch_r2	4.74
hw_gamma	0	lumpiness	0	diff2x_pacf5	0	hurst	5.45
hw_beta	0	diff1x_pacf5	0	unitroot_kpss	0	garch_acf	5.53
hw_alpha	0	diff1_acf10	0	x_pacf5	0	entropy	11.65
trend	0	arch_lm	0	x_acf10	0
flat_spots	0	diff1_acf1	0	unitroot_pp	0
series_length	0	stability	0	arch_r2	1.37

To replicate this results use:

python -m tsfeatures.compare_with_r --results_directory /some/path
                                    --dataset_name Daily --num_obs 100

Sesonal data (100 Hourly M4 time series)

feature	diff	feature	diff	feature	diff	feature	diff
series_length	0	seas_acf1	0	trend	2.28	hurst	26.02
flat_spots	0	x_acf1	0	arch_r2	2.29	hw_beta	32.39
nperiods	0	unitroot_kpss	0	alpha	2.52	trough	35
crossing_points	0	nonlinearity	0	beta	3.67	peak	69
seasonal_period	0	diff1_acf10	0	linearity	3.97
lumpiness	0	x_acf10	0	curvature	4.8
stability	0	seas_pacf	0	e_acf10	7.05
arch_lm	0	unitroot_pp	0	garch_r2	7.32
diff2_acf1	0	spike	0	hw_gamma	7.32
diff2_acf10	0	seasonal_strength	0.79	hw_alpha	7.47
diff1_acf1	0	e_acf1	1.67	garch_acf	7.53
diff2x_pacf5	0	arch_acf	2.18	entropy	9.45

To replicate this results use:

python -m tsfeatures.compare_with_r --results_directory /some/path \
                                    --dataset_name Hourly --num_obs 100

Authors

• Federico Garza - FedericoGarza
• Kin Gutierrez - kdgutier
• Cristian Challu - cristianchallu
• Jose Moralez - jose-moralez
• Ricardo Olivares - rolivaresar
• Max Mergenthaler - mergenthaler

Release information

0.2.0

• Fix Supersmoother: for non seasonal time series (freq=1) trend stl_features only returned nan.
• Set dependencies versions.