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