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.

<img src=https://raw.githubusercontent.com/FedericoGarza/tsfeatures/master/.github/images/y_train.png width="152">

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_r(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