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

Python 3.11 or newer is required. Support for Python 3.10 and earlier has been dropped.

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

pip install tsfeatures

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})

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.

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.

To replicate this results use:

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

To replicate this results use:

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

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