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https://github.com/nixtla/tsfeatures

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

https://github.com/nixtla/tsfeatures

Science Score: 13.0%

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    Low similarity (12.9%) to scientific vocabulary

Keywords

errors features fforma forecasting m4 metrics python time-series tsfeatures
Last synced: 6 months ago · JSON representation

Repository

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

Basic Info
  • Host: GitHub
  • Owner: Nixtla
  • License: apache-2.0
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 230 KB
Statistics
  • Stars: 421
  • Watchers: 9
  • Forks: 45
  • Open Issues: 8
  • Releases: 11
Topics
errors features fforma forecasting m4 metrics python time-series tsfeatures
Created over 6 years ago · Last pushed almost 2 years ago
Metadata Files
Readme License Code of conduct

README.md

Build PyPI version fury.io Downloads Python 3.6+ License: MIT

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:

python 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.

python from tsfeatures import tsfeatures

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

python 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:

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

You can use your own dictionary using the dict_freqs argument:

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

List of available features

| Features ||| |:--------|:------|:-------------| |acffeatures|heterogeneity|serieslength| |archstat|holtparameters|sparsity| |countentropy|hurst|stability| |crossingpoints|hwparameters|stlfeatures| |entropy|intervals|unitrootkpss| |flatspots|lumpiness|unitrootpp| |frequency|nonlinearity|| |guerrero|pacffeatures||

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

```python 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:

```python def number_zeros(x, freq):

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

tsfeatures(panel, freq=7, features=[acffeatures, numberzeros]) ```

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):

```python from tsfeatures.tsfeaturesr import tsfeaturesr

tsfeaturesr(panel, freq=7, features=["acffeatures"]) ```

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 | |:----------------|-------:|:----------------|-------:|:----------------|-------:|:----------------|-------:| | eacf10 | 0 | eacf1 | 0 | diff2acf1 | 0 | alpha | 3.2 | | seasonalperiod | 0 | spike | 0 | diff1acf10 | 0 | archacf | 3.3 | | nperiods | 0 | curvature | 0 | xacf1 | 0 | beta | 4.04 | | linearity | 0 | crossingpoints | 0 | nonlinearity | 0 | garchr2 | 4.74 | | hwgamma | 0 | lumpiness | 0 | diff2xpacf5 | 0 | hurst | 5.45 | | hwbeta | 0 | diff1xpacf5 | 0 | unitrootkpss | 0 | garchacf | 5.53 | | hwalpha | 0 | diff1acf10 | 0 | xpacf5 | 0 | entropy | 11.65 | | trend | 0 | archlm | 0 | xacf10 | 0 | | flatspots | 0 | diff1acf1 | 0 | unitrootpp | 0 | | serieslength | 0 | stability | 0 | arch_r2 | 1.37 |

To replicate this results use:

console 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 | |:------------------|-------:|:-------------|-----:|:----------|--------:|:-----------|--------:| | serieslength | 0 |seasacf1 | 0 | trend | 2.28 | hurst | 26.02 | | flatspots | 0 |xacf1|0| archr2 | 2.29 | hwbeta | 32.39 | | nperiods | 0 |unitrootkpss|0| alpha | 2.52 | trough | 35 | | crossingpoints | 0 |nonlinearity|0| beta | 3.67 | peak | 69 | | seasonalperiod | 0 |diff1acf10|0| linearity | 3.97 | | lumpiness | 0 |xacf10|0| curvature | 4.8 | | stability | 0 |seaspacf|0| eacf10 | 7.05 | | archlm | 0 |unitrootpp|0| garchr2 | 7.32 | | diff2acf1 | 0 |spike|0| hwgamma | 7.32 | | diff2acf10 | 0 |seasonalstrength|0.79| hwalpha | 7.47 | | diff1acf1 | 0 |eacf1|1.67| garchacf | 7.53 | | diff2xpacf5 | 0 |archacf|2.18| entropy | 9.45 |

To replicate this results use:

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

Authors

Owner

  • Name: Nixtla
  • Login: Nixtla
  • Kind: organization
  • Email: ops@nixtla.io
  • Location: United States of America

Open Source Time Series Ecosystem

GitHub Events

Total
  • Issues event: 2
  • Watch event: 58
  • Issue comment event: 10
  • Fork event: 2
Last Year
  • Issues event: 2
  • Watch event: 58
  • Issue comment event: 10
  • Fork event: 2

Committers

Last synced: about 2 years ago

All Time
  • Total Commits: 193
  • Total Committers: 4
  • Avg Commits per committer: 48.25
  • Development Distribution Score (DDS): 0.047
Past Year
  • Commits: 29
  • Committers: 1
  • Avg Commits per committer: 29.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Fede f****z@g****m 184
José Morales j****s@g****m 6
FedericoGarza F****a@g****m 2
rolivaresar o****o@g****m 1
Committer Domains (Top 20 + Academic)
github.com: 1 grupoabraxas.com: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 21
  • Total pull requests: 21
  • Average time to close issues: about 2 months
  • Average time to close pull requests: 11 days
  • Total issue authors: 17
  • Total pull request authors: 4
  • Average comments per issue: 1.48
  • Average comments per pull request: 0.38
  • Merged pull requests: 18
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 1
  • Pull requests: 0
  • Average time to close issues: 18 minutes
  • Average time to close pull requests: N/A
  • Issue authors: 1
  • Pull request authors: 0
  • Average comments per issue: 2.0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • AzulGarza (3)
  • FedericoGarza (3)
  • ngupta23 (1)
  • Saina199 (1)
  • AlbertoAlmuinha (1)
  • chillerobscuro (1)
  • Pascal66 (1)
  • rpanai (1)
  • nrcjea001 (1)
  • fgvenegas (1)
  • iLampard (1)
  • david-waterworth (1)
  • ltsaprounis (1)
  • Jean-Reinhold (1)
  • ronkremser (1)
Pull Request Authors
  • FedericoGarza (13)
  • jope35 (3)
  • tracykteal (2)
  • rolivaresar (2)
Top Labels
Issue Labels
enhancement (3) feature (3) question (1) bug (1)
Pull Request Labels
enhancement (1)

Packages

  • Total packages: 3
  • Total downloads:
    • pypi 30,373 last-month
  • Total dependent packages: 3
    (may contain duplicates)
  • Total dependent repositories: 3
    (may contain duplicates)
  • Total versions: 22
  • Total maintainers: 3
pypi.org: tsfeatures

Calculates various features from time series data.

  • Versions: 10
  • Dependent Packages: 3
  • Dependent Repositories: 3
  • Downloads: 30,373 Last month
Rankings
Downloads: 1.6%
Dependent packages count: 2.4%
Stargazers count: 4.0%
Average: 4.7%
Forks count: 6.7%
Dependent repos count: 9.0%
Maintainers (3)
fgarzar capybara MergenthalerMax
Last synced: 6 months ago
proxy.golang.org: github.com/nixtla/tsfeatures
  • Versions: 6
  • Dependent Packages: 0
  • Dependent Repositories: 0
Rankings
Dependent packages count: 6.4%
Average: 6.6%
Dependent repos count: 6.8%
Last synced: 6 months ago
proxy.golang.org: github.com/Nixtla/tsfeatures
  • Versions: 6
  • Dependent Packages: 0
  • Dependent Repositories: 0
Rankings
Dependent packages count: 6.4%
Average: 6.6%
Dependent repos count: 6.8%
Last synced: 6 months ago

Dependencies

requirements.txt pypi
  • antropy >=0.1.4
  • arch >=4.14
  • pandas >=1.0.5
  • scikit-learn >=0.23.1
  • statsmodels >=0.12.2
  • supersmoother >=0.4
setup.py pypi
  • antropy >=0.1.4
  • arch >=4.11
  • pandas >=1.0.5
  • scikit-learn >=0.23.1
  • statsmodels >=0.12.2
  • supersmoother >=0.4
.github/workflows/python-package.yml actions
  • actions/checkout v2 composite
  • actions/setup-python v2 composite
.github/workflows/python-publish.yml actions
  • actions/checkout v2 composite
  • actions/setup-python v2 composite